Band 17, Heft 1

A large-scale shaking table test of an anti-dip rock slope with a weak interlayer is conducted based on the Hongshiyan landslide induced by the Ludian earthquake. The dynamic failure mode energy damage identification method is analyzed using the Hilbert–Huang transformation and marginal spectrum theory. The result shows that the weak interlayer under horizontal loading amplifies the 5–10 and 25–30 Hz frequency signal and attenuates the 15–20 Hz signal. Within the hard rock, it amplifies the 5–15 Hz frequency signal and attenuates the 20–30 Hz signal. Under vertical loading, both weak interlayer and hard rock have an amplification effect on 5–30 Hz signal, and the amplification effect of weak interlayer is more significant. The horizontal seismic wave slope damage process can be divided into two stages: internal damage and obvious failure. When the horizontal seismic input amplitude reaches 0.2–0.3 g, internal damage occurs in the slope. The slope damage is primarily concentrated below two-third of the hard rock height and primarily in the shallow and surface regions of the slope. An input amplitude in the range of 0.4–0.5 g corresponds to the obvious failure stage. At this time, the slope first undergoes an obvious rupture in the rock above the weak layer; this rupture then extends upward to the top of the slope. Marginal spectral analysis results are consistent with the field model slope macroscopic rupture phenomenon.

By simulating the layout of the “Production–Living–Ecological space” under various scenarios in the future and exploring the trend of future land use changes, it is of great significance to optimize the land use structure and ecological environment of the region. Based on the existing land use data and combined with the PLUS model, the article predicts the land demand and distribution of the PLES in the future 2040 and 2060 and then studies the contribution rate of the area changes of each land type to the change of regional ecological environment quality. The results show that (1) agricultural production land is the main type of land use in Anyang City from 1980 to 2020, and the conversion type of land use is mainly manifested as the conversion of agricultural production land to living land. (2) In all three scenarios, the main changes between 2020 and 2060 are the contraction of production land and the expansion of living land. The change rate of the ecological protection scenario is the smallest, and the decrease rate of the urban development scenario is the largest. (3) In 2060, all three scenarios show varying degrees of reduction in the Ecological environment quality index (EQI) compared to 2020. At the same time, the change in regional environmental quality is closely related to the change in agricultural land. (4) The regional ecological environment quality is the result of multi-factors, among which annual precipitation has the strongest explanatory power, and all factors show synergistic effects. The present study is intended to provide a reference for optimizing the pattern of the PLES in the region and improving the regional environmental quality.

Chlorophyll is an important factor in crop growth, and is a good indicator of plant nutritional stress, photosynthetic capacity, and growth status. Real-time and reliable crop nutritional diagnosis is the basis for scientific fertilizer management and one of the key technologies for practicing fine agriculture. Focusing on rapeseed crops within the northwest region, this study employed correlation analysis between Soil Plant Analysis Development (SPAD) values and spectral parameters of rape leaves to identify SPAD-sensitive spectral parameters. SPAD values are units of relative chlorophyll content and are used to determine the current relative amount of chlorophyll in a leaf. Subsequently, single-factor models, partial least squares regression models, Back Propagation neural network (BPNN) models, Genetic Algorithm (GA) optimization BPNNs, and BPNN models optimized through GAs based on multiple linear stepwise regression using spectral parameters (referred to as MLSR-GA-BP NN models) were constructed and compared. Findings revealed several significant observations: (1) Consistency in the spectral curves of rape leaves, with spectral reflectance diminishing as chlorophyll content increased; (2) Strong correlations among seven spectral parameters utilized in the modeling, all exceeding 0.770 and achieving significant correlations at the 0.01 level; (3) Across various growth periods, the BPNN model optimized through GAs based on multiple linear stepwise regression emerged as the optimal model. With modeling R 2 surpassing 0.77 and reaching a maximum of 0.91, validation further demonstrated R 2 exceeding 0.73, with a maximum of 0.92, root mean square error ranging between 1.32 and 3.22, and relative error between 2.50 and 4.49%. Hence, the BPNN model optimized by GAs based on multiple linear stepwise regression proves to be an effective inversion method for accurately and swiftly estimating SPAD values in rape leaves.

In the Nilgiris district of the Western Ghats, landslides pose a recurrent threat, necessitating accurate landslide susceptibility mapping (LSM) to designate high-risk zones and mitigate the potential loss of lives and property. Kundah taluk in the Nilgiris has witnessed an increasing number of landslide events in recent years, primarily attributed to heavy monsoon rainfall, steep terrain and human-induced factors. This highlights the necessity for mapping landslide susceptibility and effective planning. Two bivariate statistical models were employed to evaluate the landslide susceptibility: Frequency ratio (FR) and Shannon entropy (SE). A comprehensive database of past landslides was compiled using satellite imagery and surveying the study region. In total, 581 landslide locations were identified. Two datasets containing 407 landslides (70% of the total) for model development and 174 landslides (30%) for validation were randomly selected from the total number of landslides using GIS. Slope, aspect, soil, elevation, land use and land cover, drainage density, normalized difference vegetation index, geomorphology, road, lineament density and rainfall patterns were the 11 landslide conditioning elements considered for the modeling process. Validation of the FR and SE models using the receiver operating characteristic curve yielded prediction accuracies of 81.6 and 82%, respectively. The SE model was the most realistic and reliable for landslide identification and prediction, followed by the FR model. The derived LSMs for the area can enhance decision-making in landslide management and guide strategic planning for the Kundah taluk in the Nilgiris, ultimately helping to prevent future landslide events.

Investigating the origin and accumulation of natural gas is crucial for investigating fields of gas and oil. This study can provide effective guidance for future strategic decisions. In this study, the Changxing–Feixianguan Formation gas reservoir in the Puguang area was employed as a case study. The gas source strata were evaluated through analysis of the geochemical characteristics of natural gas. The period of accumulation and reservoir type were identified by examining the inclusion and burial history characteristics. The results revealed that (1) the Changxing–Feixianguan Formation was a high-sulfur gas reservoir, primarily characterized by oil-type gas. Influenced by thermal sulfate reduction, it exhibited partial characteristics of a mixed gas; (2) the natural gas of the Changxing–Feixianguan Formation demonstrated a favorable biogenic relationship with the carbon isotope characteristics of the Longtan Formation and Maokou Formation kerogen, and its primary gas source was Permian source rock; and (3) the Changxing–Feixianguan Formation gas reservoir experienced three oil and gas charging events, namely, the Middle Triassic, Late Jurassic, and Late Cretaceous. Natural gas was primarily extracted from ancient reservoirs and has evolved into light oil, condensate gas, wet gas, and dry gas. These findings serve as a foundation for investigating the accumulation of natural gas within the Changxing–Feixianguan Formation in the Puguang area. In future natural gas exploration efforts, it is recommended to concentrate on identifying paleostructures that may have formed ancient oil reservoirs to enhance the precision of natural gas exploration.

The Lembang Fault, located north of Bandung in West Java, Indonesia, is an active fault that can pose a significant earthquake hazard. The Fault extends 29 km in an east-west direction and is capable of generating earthquakes of magnitude 6.5–7.0 based on surface geological observations and previous paleoseismological studies. In earthquake mitigation, it is crucial to accurately describe the geometry of potential earthquake sources. Therefore, a subsurface model supported by high-resolution data is necessary to adequately characterize the geometry of the Lembang fault. Love wave ambient seismic noise tomography was used in this study to create a seismic velocity model based on data from 74 recording stations. The model accurately characterizes the high velocity contrast and low shear wave velocity anomalies associated with the Lembang Fault Zone. Pronounced velocity anomalies are observed, suggesting that they are related to the fault plane, which is confirmed by seismic activity in the region. In addition, the evidence has been found for another possible fault. Lembang fault has two fault planes: One is a vertical fault and the other is a south-dipping thrust fault. This fault is a cause for concern as it has the potential to generate earthquake with significant consequences.

A recently introduced rainfall threshold for landslide early warning systems combined a cumulative rainfall threshold with a rainfall event–duration (ED) threshold. Cumulative rainfall with rainfall event–duration, known as the CED threshold, was reported to perform better than the conventional ED threshold. However, the establishment of the CED threshold was based on a frequentist approach which required an adequate number of landslide-triggering rainfall data. An alternative to the use of landslide-triggering rainfall data is the use of non-triggering rainfall data. These rainfall events supply much bigger amount of data to the susceptibility model. Although the establishment of a rainfall threshold based on non-triggering rainfall events is seldom considered, previous scholars reported that this approach has produced better results than the conventional approach based on landslide-triggering events. This study investigates the reliability and prediction performance of the CED threshold based on non-triggering rainfall data. The performance of this threshold, designated as the negative-CED (CED N ) threshold, was compared with the positive-CED (CED P ) threshold based on landslide-triggering rainfall data. North Thailand, a landslide hot spot, was chosen as the study area. The proposed threshold was assessed from three skill scores, including (1) the true positive fraction (TPF), (2) the false positive fraction (FPF), and (3) the positive predictive value (PPV), and their variations over the range of threshold uncertainties. Rather than possessing lower uncertainties of the threshold parameters, the negative threshold provided better compromise predictions of TPF and FPF scores than the positive thresholds. Integrating the cumulative rainfall threshold and an event-based rainfall threshold resulted in a significant improvement in FPF scores, and hence enhanced the compromise predictions of TPF and FPF scores. Keeping in mind that the negative thresholds were not established from landslide data, care must be taken when using these thresholds and it is recommended that they should be applied only to areas where landslide data are limited.

Land use and land cover (LULC) changes are important for gaining a perspective on environmental dynamics and the impact on climate, urbanization, and resources. To ensure that it is safe to monitor the changes over time and to adopt the right forceful changes in our area, remote sensing is one of the ways to monitor the local and regional level land use, land cover patterns, and landscape changes. This study investigates the temporal LULC changes in the Nowshera region of Pakistan for the years 2016–2023 using pixel and region-oriented classification methods. As a first step, freely available high-resolution multispectral data of Sentinel-2 satellite are acquired, which serves as input dataset for both pixel and region-oriented classifiers. The accuracy assessment scores confirm that for the classified data of the year 2016, the region-oriented technique demonstrated higher overall classification accuracy (89.6%) over pixel-based classification (80.77%). Moreover, for the dataset of the year, the region-oriented method achieved a higher overall Kappa hat score (0.88) as compared to the pixel-based method (0.71). Similarly, for the classified data of the year 2023, the region-oriented method achieved higher scores for both the overall accuracy and Kappa hat (93.6 and 0.92%) over the pixel-based method (77.18 and 0.66%). The study states that for the assessment of LULC changes in Nowshera, the region-oriented image analysis provides a higher level of classification accuracy than the pixel-based approach. These results illustrate that this tool is particularly effective in monitoring detailed land cover transformations, thereby enhancing the quality of environmental management. Furthermore, the regression analysis reveals a substantial correlation between LULC changes and alterations in temperature and precipitation, and this result suggests the necessity of the development of specific climate adaptation programs.

Arid/semi-arid regions face water challenges much like the Arabian Peninsula, which is primarily caused by continuing shortages and growing activities of reclaimed land, as well as industrial and domestic activities. Consequently, identifying groundwater prospective zones (GWPZs) has become essential for securing water resources. The study aims to delineate and predict the best areas of groundwater prospection and abstraction by implementing the analytical hierarchy process-geographic information system (GIS) techniques in a rough terrain that occupies ∼70% of fractured hard rocks including ∼34% of the basaltic flow sheet of Wadi Marawani, Saudi Arabia. To investigate the combined impact of the model, 13 input thematic maps, including elevation, slope, curvature, depression, drainage density, Topographic Wetness Index, distance to river, Stream Power Index, Terrain Roughness Index, geology, lineaments, Normalized Difference Vegetation Index, and rainfall factors, were created, and employed in the model, which was subsequently merged through GIS techniques to reveal prospective zones. These maps are mainly derived from Shuttle Radar Topography Mission, Sentinel-1, Landsat, and Tropical Rainfall Measuring Mission. The output map is categorized as very low, low, moderate, high, or very high, and excellent occupying ∼7%. This promising zone is the result of the intersection of several criteria that control groundwater occurrences. The results were enhanced by implementing optical and radar remote sensing data, and thus, suitable recharge places for the future governance and abstraction of groundwater have been identified using GIS–AHP–multicriteria decision analysis methods. For validation, large numbers of well/spring locations that reached 415 are used in total. The efficiency of the model is estimated at 79.90% (area under curve) based on the receiver operating characteristic curve. Moreover, the Interferometry Synthetic Aperture Radar coherence change detection image validated the predicted model and revealed areas of no-coherence areas marked in brown matched to vegetated areas and excellent zones of GWPZs. The applied methodologies and findings of this study present significant insights for water resources planning and management to develop groundwater resources in similar regions worldwide.

Due to the tight sandstone widespread development of nanoscale pore-throat systems, the microscopic pore-throat characteristics of tight reservoirs are the focus of research. Taking the main tight oil production province of the Ordos Basin in China as a case study, nuclear magnetic resonance (NMR), pressure-controlled mercury injection (PMI), scanning electron microscopy, and micro-computed tomography (μCT) are used to analyze the characteristics of four types of tight sandstones with different pore throats. Furthermore, the effects of pore throats on movable fluid, connectivity, and reservoir physical properties are analyzed. The results show that the pore throats of the four types of tight sandstones are obviously different, but they are all dominated by nanoscale pore-throat systems. From types I to IV, the pore types of the tight sandstone change from residual intergranular pores to the coexistence of feldspar dissolved pores and residual intergranular pores and then to the coexistence of residual intergranular pores and intergranular micropores. The T 2 NMR spectrum changes from a double peak to a single peak, the pore-throat connectivity revealed by μCT decreases from 83.8% for type I to 13.1% for type IV, the pore-throat volume ratio revealed by PMI decreases from 2.09 to 0.43, and the NMR movable fluid saturation decreases from 38.91 to 12.39%. Tight sandstone with larger pores and a uniform distribution has high movable fluid saturation and good pore-throat connectivity. Although the tight sandstone with dissolved pores and intergranular pores may have a medium porosity, the pore–throat connectivity deteriorates compared with high porosity sandstone. Large pore throats, which account for less than 15% of all pore throats, contribute more than 90% of the permeability, while pore throats less than 0.8 μm in diameter are major contributors to the reservoir space, and both the porosity and permeability decrease as the pore-throat diameter decreases.

Coal mining in ecologically fragile areas results in the failure of aquiclude layers and the loss of surface water bodies. Herein, research was conducted on the microbial solidification of cracked soils and the corresponding response of the ecological water table. A simulation of mining-induced cracked soils was performed via microbial solidification. The mechanical and hydrological properties of cracked soil samples repaired with different filling materials were compared via unconfined compressive strength and falling head permeability tests. Hydraulic-electric similarity modeling techniques were employed to evaluate the effectiveness of microbial solidification in the aquiclude layers. After low-temperature acclimation, Bacillus megaterium adapted to the geological environment of the study area, exhibiting a high viable cell density. When the cracked soil was filled with a 1:1 ratio of aeolian sand to clay particles, the microbially remediated soil demonstrated optimal mechanical and hydraulic properties. Hydraulic-electric similarity numerical simulations revealed that the ecological water table at the coalface remained within a reasonable range following microbial solidification, suggesting that microbial solidification achieved water-preserving coal mining. These findings provide a reference for restoring aquiclude layers damaged by coal mining.

Exploring the coupling relationship of “pattern–process–quality” is conducive to understanding the internal mechanism of habitat quality change, and is of great significance for the function maintenance and sustainable management of regional ecosystems. Existing studies mainly analyze the spatial-temporal evolution of habitat quality from the perspective of “pattern quality” and land use data. However, the variation in habitat quality is the result of many factors such as “pattern–process–quality” of the habitat. Therefore, it is necessary to consider these factors comprehensively when studying the change in habitat quality, so as to understand the change mechanism of habitat quality more comprehensively and deeply. This study takes Cambodia, a tropical monsoon climate region, as the research area, and uses land cover data from 2000 to 2022 as the data source. InVEST model is used to analyze the spatial-temporal evolution of habitat quality, and explore the relationship between habitat quality and land use, landscape pattern, and ecological process. The results show that (1) during 2000–2022, forest in Cambodia covered a wide range, showing a landscape pattern mainly distributed in the east and west. (2) The degree of fragmentation of impervious landscape pattern increased gradually from 2000 to 2022, indicating that it was seriously affected by human activities and natural factors, and with the increase in elevation and slope, the area of various types converted to impervious decreased. (3) During 2000–2022, high habitat quality areas were concentrated in Tonle SAP Lake in the east, southwest, and central part of Cambodia, while low habitat quality areas were concentrated in the central part. (4) Natural factors, socio-economic factors, policies, and regulations all have an impact on the change in land use pattern and habitat quality in Cambodia.

The organic geochemical and petrological analyses of calculated vitrinite reflectance of the Early Permian to Middle Triassic Formation section of the Sydney Basin in Australia have revealed the source for the widely distributed oil and gas shows in the basin. The coal formations of the Permian in the Sydney Basin have a higher hydrogen index with much higher TOC extracts than the non-coal formations of the Permian in the same basin. Some source-specific parameters attained from biomarkers, such as n -alkanes, regular isoprenoids, terpanes, diasteranes, and steranes, show that oil was generated and expelled mainly from organic matter derivatives of higher plants deposited in the oxic environment. The biomarkers associated with maturity, source, and aromatic hydrocarbon are mostly from coal core samples. However, extracts from non-coal samples have an organic geochemical signature, indicative of mixed terrestrial and prokaryotic organic matter deposited in a suboxic environment. Significantly, it is different from the coal extracts. Still, the molecular signature of some of the coal samples shows prokaryotic organic matter present in the extracts, indicating a thin section of possibly a calcareous lithology interbedded within the coal measure formation. Genetically, the coal extracts of the Permian in the Sydney Basin, Australia, provide a new insight into the generation and expulsion of oil from coal, which might be responsible for the widespread oil and gas shows in the basin. This raises the possibility of commercial oil accumulation in the early Permian coals to Middle Triassic sandstones, perhaps in the deeper part of the offshore basin.

On December 18, 2023, the M S 6.2 Jishishan earthquake triggered a large-scale liquefaction disaster of loess sites in Jintian and Caotan villages, Zhongchuan town, Minhe County, Haidong City, Qinghai Province. To clarify the micro-mechanism of the liquefaction disaster, the Q 3 Malan loess layer of the disaster site and its overlying red silty clay layer samples were selected and quantitatively analyzed for the differences in physical properties, structure, microstructural parameters, and mineral compositions. Based on the discrepancy results, the micro-mechanisms between loess microstructure and macro-mechanical properties of soil and liquefaction disaster were investigated. The research shows that compared with red silty clay, the dynamic index of loess corresponding to the physical indices of Zhongchuan loess obviously exceeds the critical threshold of liquefaction under actual seismic intensity. Additionally, its pore structure is dominated by point contact and weakly cemented overhead macropore structure, and its quantitative pore microstructure parameters and mineral composition show significant liquefaction potential. The comprehensive analysis of the liquefaction mechanism shows that the rapid deformation of the soil skeleton and the destruction of the cementation and contact system of the water-sensitive minerals under seismic loading and hydraulic force lead to the collapse of the overhead macropores, the damage of structural strength, the increase of the complex pore channels, the rapid accumulation of pore water pressure, and the reduction of the effective stress, which leads to the liquefaction of the loess.

S-wave velocity, among the critical parameters essential for developing 3D/4D seismic forward models, is prominent. Variations in both P- and S-wave velocities result from changes in the formation pressure and fluid saturation inside reservoirs during CO 2 geological storage operations. This study, understanding the significant variety and uneven stress distributions of tight sandstone reservoirs in the Ordos Basin, begins with developing a predictive model for S-wave velocities. The model integrates the Digby and DEM models and takes into account the changes in the formation pressure and alterations in pore shapes. Data from petrophysical experiments are used to validate this model. By comparing the shear wave velocity prediction results under four pore shapes, spherical, needle-shaped, disc-shaped, and coin-shaped gaps, the dominant pore shape of this sedimentary facies belt was selected, and the pore shape of the target layer was identified as needle-shaped pores. The shear wave velocity prediction model for this area was then optimized. Moreover, by analyzing actual well logging data, the methodology is validated and it shows high accuracy when using the dominant pore shapes to predict the S-wave velocity. This study emphasizes how important it is to take geological factors into account when developing 3D/4D seismic petrophysical predication models of S-wave velocities specifically designed for tight sandstone reservoirs.

Coal seams in valley areas are shallow buried, and mining activities have a strong impact on the surface environment, so ecological restoration in this condition is very difficult. Strengthening the monitoring of ecological changes plays an important role in optimizing mining methods and ecological restoration. In this article, a valley area in the northern Shaanxi Province of China was selected as the research area, and a similar ecosystem around the study area was selected as the reference subject. The traditional remote sensing ecological index model was improved, and a new evaluation model was constructed based on the framework of Modified Remote Sensing Ecology index-Deviation from Reference Ecosystem Index-Ecological Restoration Quality Index (ERQI) to study the evolution of the environment in the study area from the perspective of the whole life cycle of mining activities. The results showed that ERQI values in the study area experienced an evolution process from high to low and then gradually increased, reflecting the impact of coal mine production and restoration activities on the environment, in which Burn Area Index and normalized difference vegetation index were more sensitive; after nearly 10 years of restoration, ERQI values in the study area still had a large gap with that of the reference area, some suggestions for improvement of ecological restoration were put forward. The research provided a quantitative approach to describe the evolution of the ecological environment and a scientific basis for the ecological restoration in the mining areas.

The characteristics of coal-bearing source rocks in the continuous sedimentary environment of the Xujiahe Formation are discussed in the Southwest Sichuan through the thermal simulation and organic geochemical experiments, and the quantitative relationships are determined between carbon isotopes, maturity, and distinct organic matter (OM) types. The results show that the source rocks of the first member and third member of the Xujiahe Formation are mainly formed in the transitional sedimentary environment, while the source rocks of the fifth member of the Xujiahe Formation are primarily developed in the continental sedimentary environment, controlled by the continuous sedimentary environment of the Xujiahe Formation. The vitrinite and inertinite content increases with the liptinite content decreasing from the first member to the fifth member of the Xujiahe Formation. Moreover, the OM of the source rocks is typed II 2 in the first member of the Xujiahe Formation, with the III OM in the third member and fifth member of the Xujiahe Formation. Additionally, the quantitative relationship is identified between methane carbon isotopes and maturities of the humic and mixed gas. The quantitative relationship is presented at δ 13 C 1 = 12.21 ln Ro – 39.72 (humic gas) and δ 13 C 1 = 8.07 ln Ro – 38.3 (mixed gas). The ethane carbon isotopes range from −24.93 to −21.07‰ in the first member and the third member of the Xujiahe source rocks, and are between −29.31 and −26.78‰ in the fifth member of the Xujiahe source rocks, which can effectively distinguish the OM type by ethane carbon isotopes. The emergence of new recombined heavy hydrocarbons leads to the carbon isotope inversion of heavy hydrocarbon in the high thermal evolution stage. This study has contributed to the hydrocarbon-generating characteristics of source rocks in the continuous sedimentary environment and the fine gas source correlation.

Coseismic surface displacements pose a serious threat to the safety of linear infrastructures on active faults. A reasonable evaluation of surface displacements on active faults is important. Probabilistic fault displacement hazard analysis (PFDHA) is often used for surface displacement evaluation. However, PFDHA, developed based on the classical probabilistic method, requires an in-depth study of the activity parameters of all active faults in seismic–tectonic zones, greatly limiting its application in engineering design. The recurrence interval of strong earthquakes is a readily obtainable parameter of fault activity. In this study, we combine this key parameter with various seismic indicators to develop a comprehensive algorithm to assess potential future surface displacement hazards. In addition, the factors affecting the method are analyzed, and the surface displacements of several earthquakes with a magnitude of 7 or above are compared. The results indicate that the predicted future surface displacements using our method are greater than the true displacements of seismic events. For easy use, the exceedance probability–displacement (horizontal and vertical) curves of 27 strong earthquake recurrence intervals are given. This facilitates structural designers to quickly obtain future displacement values in structural design.

The Zalm district in western Saudi Arabia has drawn significant attention from those interested in exploring for gold mineralization. In the present study, the structural framework and major structural trends that most likely control the distribution of the gold mineral deposits in the Zalm district are interpreted using aeromagnetic data. For this purpose, the aeromagnetic data were subjected to enhancement filters to make the structural interpretation of the data easier. In this respect, the data were reduced to the north magnetic pole and processed through two enhancement filters: the analytical signal (AS) and the first vertical derivative (1VD). Lineament analysis of aeromagnetic maps suggests that three major fault sets dissected the Zalm district trending in the northwest (NW), northeast (NE), and north-south (NS) directions. These faults likely affected the mineralization occurrences in the Zalm district and could serve as important structural conduits for hydrothermal fluid channeling. Based on the current results, four localities were identified as promising for additional mineral resource investigation. These localities are delimited and dissected by the NW, NE, and NS faulting and are intruded by a series of felsic and mafic–ultramafic intrusions. It is likely that, in similar to Bi'r Tawilah where the location is structurally controlled by NW and NS faulting, the interpreted felsic and mafic served as sources of mineralizing fluids which remobilized the ore in the district by channeling through the faults. Three of the suggested localities, one lying in close proximity to Zalm city and two along the Rada and Nabitah fault zones, are characterized by high magnetic anomalies, indicating the presence of ultramafic and volcanic rocks that most likely host mineralization. The fourth locality situated in the central part of the district is marked by an elongated N–S low magnetic anomaly, interpreted as felsic diorite and granite rocks delimited by N–S faulting. To accurately detect mineral resources in the selected zones, additional comprehensive and in-depth geological and geophysical research are recommended.

The sediment particles on muddy coasts are relatively fine, and they are easy to move under the dynamic action of wave power, which can easily cause sedimentation problems in port channels. Therefore, the problem of silt initiation is a key issue for in-depth research in the development process of silt coast harbors. Based on a large amount of experimental data on silt initiation in silty coast conducted by predecessors, the relationship between the incipient Shields number and sediment parameters such as sediment particle size and sediment density was quantitatively analyzed. The explicit calculation formula of the incipient Shields number is established, and the modified distribution map of the incipient curve zone is drawn. The role of particle size and sediment density in sediment incipient motion was elucidated. The modified distribution map of the incipient motion curve was drawn, which promotes the application of the Shields curve in muddy coastal engineering.

Water resources are regarded as the primary impediment to proper human development in arid and desert regions, and the situation has been exacerbated by current climate change. The same phenomenon can be observed in the Ziban region of the Biskra wilaya, where agriculture consumes the most water resources. It is due to the increase in the size of reclaimed agricultural areas from year to year as the region’s urban centres expand, causing wastewater to be discharged into agricultural sewers and discharged into the Oued Djedi. However, during our field survey, we discovered that some farmers water their crops with drainage water mixed with wastewater. This study aims to determine how climate change is affecting the region, as well as the damage caused by watering crops with agricultural and sanitary drainage water. A statistical analysis of climatic parameters (mainly rainfall) and physiochemical parameters of drainage water at six sampling points in the Occidental Zab region was carried out. We measured nitrates, calcium hardness, chloride ions, nitrates, chemical oxygen demand (COD) and biochemical oxygen demand (BOD5). The results show a reduction in the total annual rainfall rate, from 208.6 mm in 1996 to 19 mm in 2017. As for drainage water analysis, the following results were noted: SO 4 2− (mg/l) [545.45–3436.36], NO 3 − (mg/l) [0.75–3.94], BOD5 (mg/l) [10–50], COD (mgO 2 /l) [14.12–72.25], Cl − (mg/l) [331.33–1692.16], Ca 2+ (mg/l) [352.68–643.23], Mg 2+ (mg/l) [120]. The study emphasises the importance of a comprehensive assessment of wastewater for agriculture, particularly because some farmers use it to water their crops, as well as the need for targeted interventions to mitigate potential challenges associated with potential damage. As a result, these findings can help decision-makers and local stakeholders establish wastewater filtration plants, improve farmers’ water use, and protect this valuable resource.

As-built mapping using terrestrial sensors is crucial for accurately documenting the final state of building structures. However, conventional methods often face limitations in urban environments, such as obstructions and the need for clear sightlines. This study evaluates the effectiveness of the low-cost Moasure ONE Inertial Measurement Unit sensor for producing as-built plans, addressing challenges associated with traditional surveying methods like total stations. The research investigates the Moasure ONE’s capabilities in terms of cost, time efficiency, flexibility, and productivity, while adhering to the USA Federal Geographic Data Committee and American Society for Photogrammetry and Remote Sensing accuracy standards. The methodology includes a statistical study, data manipulation, georeferencing, and quality assessment using ground control points. Results indicate that Moasure ONE can achieve a horizontal positional accuracy (RMSE H1 ) of 17.7 cm, aligning with the required tolerances for detailed mapping. The findings suggest that Moasure ONE offers significant advantages over traditional methods, including reduced survey time and increased detail capture, making it a viable alternative for professional surveying tasks in construction and engineering projects.

In the Loess Plateau, farmers usually collect the surface soil of terrace sections for use as a fertilizer. Samples were taken at the Loess Plateau in Yan’an City to analyze the distribution patterns on the terrace section of different types, including different years (2, 6, and 10 years), slope aspects (shady slope and sunny slope), vertical heights (0.2, 1, and 1.8 m from the ground), and depths (0–1, 1–3, and 3–5 cm from the surface soil). The results showed that (1) with the increase in years, under the influence of natural factors such as temperature, sunlight, and moisture, the soil nutrients on the surface of terrace section were gradually activated and enriched, and the enrichment effect of nutrients on the 10-year terrace section was the most significant. (2) Under the influence of natural factors such as temperature and sunlight, as the depth of the terrace section from the soil surface increased, the values of available potassium and total nitrogen gradually decreased. The nutrients on the terrace section were mainly enriched in the surface soil and could be used as potassium and nitrogen fertilizers. (3) In the study area, when the heights from the ground surface were different, the differences in nutrients on the terrace section were not significant. The surface soil of the terrace section was measured to be rich in nutrients such as nitrogen and potassium. The research results confirmed the scientific nature of local farmers’ collection of fertilizers from the terrace section and have important theoretical reference value for the rapid ripening of raw soil in land remediation projects and the production of ecological fertilizers.

Effective management of watershed risks and landslides necessitates comprehensive landslide susceptibility mapping. Support vector machine (SVM) and random forest (RF) machine learning models were used to map the landslide susceptibility in Morocco’s Taounate Province. Detailed landslide inventory maps were generated based on aerial pictures, field research, and geotechnical survey reports. Factor correlation analysis carefully eliminated redundant factors from the original 14 landslide triggering factors. As a result, 30% of the sites were randomly chosen for testing, whereas 70% of the landslide locations were randomly picked for model training. The RF model achieved an area under the curve (AUC) of 94.7%, categorizing 30.07% of the region as low susceptibility, while the SVM model reached an AUC of 80.65%, indicating high sensitivity in 53.5% of the locations. These results provide crucial information for local authorities, supporting sound catchment planning and development strategies.

Salt efflorescence was known to be one of the main causes of the irreversible deterioration of stone cultural heritage. However, the source of salts in Yungang Grottoes is still a controversial issue. Partial immersion experiments of sandstone cores in relation to salt-rich groundwater with pH = 6–7 and pH = 7–8 had been executed, involving a comparative immersion in deionized water with pH = 7–8 at equivalent exposure conditions and identical solution supply. From macroscopic and microscopic perspectives, the changes in the appearance of sandstone specimens and morphologies of salt precipitates were recorded. Salt efflorescence was visible as a result of capillary migration and evaporation, especially when the sandstone specimen was immersed in salt-rich groundwater. The sulfates in precipitates from the samples immersed in groundwater showed an abundance of epsomite and gypsum. Nevertheless, a small amount of sulfates was detected at the surface of sandstone immersed in deionized water. A noticeable increase in the amount of gypsum by 4.15% and epsomite by 18.34% was detected as partially immersed in acidic groundwater with a n (Ca/Mg) of 0.675. It was concluded that SO 4 2− in the precipitates mainly originated from groundwater, and the chemical dissolution of minerals can be recognized as an effective agent in salt-induced weathering of Yungang Grottoes.

It is of great significance to clarify the shale gas occurrence mechanism under formation condition for the exploration and development of transitional shale gas reservoir. Since the formation temperature and pressure are higher than that in facilities, to reveal the methane adsorption characteristics in Longtan Formation, the organic and inorganic slit pore models in shale were constructed by molecular simulation technology. The grand canonical Monte Carlo (GCMC) method and the molecular dynamics method were used to simulate methane’s adsorption behavior on inorganic minerals and graphite. The research results found that the methane isosteric heat on graphite, montmorillonite, illite, and quartz with different pore sizes is less than 42 kJ/mol, indicating that the methane adsorption on transitional shale belongs to physical adsorption. The adsorption capacity of the Longtan Fm. transitional shale main components is in the sequence of organic matter > clay mineral > quartz. The adsorption of methane on transitional shale is not rigorous monolayer adsorption, but mainly a strong adsorption layer. The effect of temperature on the total methane adsorption capacity is stable, and the excess adsorption capacity is greatly affected by pressure. The smaller the pore size, the higher the methane adsorption heat, the stronger the adsorption capacity of methane, and the excess and total adsorption capacity decrease with pore size decreasing. The transitional shale with a high content of organic matter and clay has high adsorbed gas content. Lower temperature and appropriate pressure are conductive to the adsorbed gas preservation.

This study examined the spatiotemporal characteristics of extreme maximum temperature events (EMTEs) in Central China using observational data from national meteorological stations and data from coupled model intercomparison project phase 6 models and by focusing on the variances between global warming thresholds of 1.5, 2.0, and 4°C. The extreme maximum temperature threshold was determined based on the 99th percentile of daily maximum temperature, and an improved intensity–area–duration method was employed to determine EMTE characteristics. Results indicated that during the reference period (1995–2014), the intensity, coverage, duration, and number of hot days of EMTEs in Central China increased by 0.19°C/10a, 1.6 × 105 km 2 /10a, 0.2 days/10a, and 3 days/10a, respectively. Compared with the reference period, under future scenarios of shared socioeconomic pathways SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-6.0, and SSP5-8.5, the intensity, coverage, duration, and number of hot days of EMTEs increased by 0.05–0.21°C/10a, 0.54–4.3 × 105 km 2 /10a, 0.12–1.19 days/10a, and 0.7–2.2 days/10a, respectively. Regionally, projected high values of intensity, duration, and number of hot days of EMTEs were mainly concentrated in eastern Henan, northeastern Hubei, eastern Hubei, eastern Jiangxi, and central regions. For warming of 1.5, 2.0, and 4.0°C, the larger the warming threshold, the greater the likelihood of EMTEs. Considering the increasing probability of more severe EMTEs, it is recommended that research on the monitoring, early warning, and forecasting of EMTEs be strengthened, and that effective measures be taken to limit the increase in global temperature to within 1.5°C to mitigate the impacts of such events.

This study presents a remote sensing approach to estimating the Bowen ratio (BR), focusing on its application across landscapes in a small-scale region of Olomouc, Czech Republic. Traditionally, BR has been calculated using in situ measurements, which restrict spatial coverage and scalability. Using satellite imagery, this research shows a spatially explicit approach to estimating BR, creating a departure from conventional techniques. The framework integrates remote sensing techniques, including atmospheric corrections and the derivation of land surface temperature, albedo, and net radiation, to generate higher-resolution BR estimates. This creative approach bridges a critical gap in local-scale BR estimation by enabling cost-effective, repeatable, and scalable analyses across varying land cover types. The findings not only highlight the spatial variability of BR but also demonstrate the transformative potential of remote sensing in advancing our understanding of land–atmosphere interactions. This pioneering method opens new pathways for sustainable management of water and energy resources while offering fresh insights into regional energy fluxes and hydrological processes.

Multi-scale 3D geological modeling technology is a vital issue to illustrate the complex geological conditions of infrastructure projects at the regional scale, engineering scale, and outcrop scale. It is also the computational basis for numerical geotechnics and seepage stabilization studies. However, empirical interactive modeling methods based on expert knowledge are mostly applied in existing numerical researches and geological structures at different scales are modeled independently, which reduces the credibility of simulation. Therefore, this research states a 3D fusion modeling method of multi-scale geological structures: (1) The multi-constraint NURBS modeling method for multi-valued strata at regional scale and the discrete fracture network modeling method for discontinuities at outcrop scale are presented. (2) The subdivision-NURBS modeling method for multiple genera geological bodies at engineering scale is raised so that the genus characteristics of geological bodies can be expressed in an objective parameterized way rather than in an empirical interactive-modeling way. (3) The Enhanced Boolean Logic Sequences of Oriented Geological Interfaces (E-BLSOGI), where the multiple genera geological bodies at engineering scale and the discontinuities at outcrop scale are additionally introduced based on the BLSOGI method offered in authors’ previous study [1], is provided to achieve 3D fusion modeling of multi-scale geological structures. The practice indicates that the 3D fusion modeling of multi-scale geological structures is realized, evidencing that results without integrating multi-scale geological structures underestimate the compressive stress (16.37, 10.52, and 33.09%), tensile stress (16.57, 21.57, and 10.76%), and displacement (7.17, 47.62, and 33.62%) of the dam foundation in x -, y -, and z-, directions, respectively.

Land use/land cover (LULC) dynamics play a crucial role in understanding the complex interactions between ecosystems and climate. This study demonstrates the effective integration of Google Earth Engine (GEE) and machine learning (ML) algorithms for monitoring LULC changes in two rapidly urbanizing cities in Bangladesh. By combining Landsat imagery with classification and regression trees, random forest (RF), and support vector machine algorithms within the GEE platform, we analyzed LULC changes from 2001 to 2021. Our analysis revealed significant urban expansion in both cities, with built-up areas showing the highest increase, while natural land covers experienced notable declines. The RF classifier consistently demonstrated superior performance, with the overall accuracy exceeding 93%. The GEE-based approach significantly reduced the processing time compared to traditional methods, while the integration of multiple ML algorithms enhanced the classification accuracy. This research advances environmental monitoring by showcasing the effectiveness of cloud-based geospatial analysis for rapid and accurate LULC change detection. The methodology presented herein offers valuable insights for urban planners and policymakers, particularly in rapidly urbanizing regions, contributing to Sustainable Development Goals 11 (Sustainable Cities and Communities) and 15 (Life on Land).

Expansive soil is a type of unsaturated soil that develops fissures within its body due to dry and wet cycles. These fissures are the primary factor contributing to slope instability in expansive soil regions. In this study, we conducted research on the impact of herbaceous plant root systems on soil properties in the Shangluo area of Shaanxi. This area is known for its distribution of expansive soils. We achieved this by planting three types of commonly used slope protection plants. The results revealed significant differences in root system characteristics among the three herbaceous plants: Lolium perenne , Cynodon dactylon , and Medicago. Particularly, Lolium perenne exhibited a well-developed and heterogeneous root system with a substantial number of roots. These roots were primarily concentrated within the interval of 0 < L ≤ 0.5 mm. The specific gravity of soil tended to decrease after herbaceous plant roots were planted. The growth of herbaceous plants greatly increased the soil’s ability to hold water. The presence of herbaceous root systems significantly improved the soil consolidation effect. The entanglement and interpenetration effects of the plant root system had a significant influence on the dispersion of soil macroaggregates and the formation of soil fabrics. The colloid content of the soil increased with the growth of the plant root system. Additionally, the root system of herbaceous plants greatly enhanced the angle of internal friction and cohesion of soil. The growth of herbal plants significantly enhanced the properties of expansive soils by improving the soil structure, increasing the nutrient richness, enhancing soil water retention, and augmenting both soil agglomeration and internal friction angles, thereby improving the soil’s solid capacity. This study elucidates the mechanisms by which herbaceous plants exert long-term beneficial effects on expansive soil areas, contributing to the prevention and control of regional ecological security.

Well-recorded earthquakes located along the rift axis of the Red Sea, western Saudi Arabia, were investigated. These events are among the most significant recorded between 1997 and 2024 in this seismic zone. The moment tensor inversion, using full waveform data, was applied to determine the rifting mechanism and parameters of the investigated earthquakes. The optimum double-couple mechanism for investigated earthquakes revealed normal faulting with minor strike slip movement along the red sea spreading axis. The most recent earthquakes were felt in nearby cities, prompting a study to understand the fault mechanisms and assess the impact of strong ground motion parameters on western Saudi Arabian cities, which have rapidly developed along the Red Sea’s active rifting system. The stochastic simulation method, based on the site spectral model, was used to estimate the effects and strong ground motion parameters of the most recent and significant earthquakes in the western cities of Saudi Arabia: one on June 16, 2020 ( M L {M}_{{\rm{L}}} = 5.5), and another on April 14, 2023 ( M L {M}_{{\rm{L}}} = 4.5). The results suggest that Al-Muwaileh and Duba cities experienced peak ground accelerations of 9.113 and 8.673 cm/s 2 from the June 16, 2020, earthquake and 2.347 and 1.707 cm/s² from the April 14, 2023, earthquake, respectively. The pseudo-seismic spectral acceleration values were 2.282 × 10 1 and 2.379 × 10 1  cm/s 2 in Duba and Al-Muwaileh, respectively, for the June 16, 2020, earthquake and 4.837 and 6.752 cm/s 2 for the April 14, 2023, earthquake.

In this article, we provide new biostratigraphic and structural data of the foredeep-derived Falterona Unit in the Arezzo area (Italy), to reconstruct the steps of evolution of the fold-and-thrust front-foredeep Apennine system. The sedimentary succession has been deposited in the N-S trending, foredeep basin bounded westward by the Apennine orogenic wedge and eastward by the Adria continental margin forebulge. The Falterona Unit mainly consists of the Monte Falterona Fm., a succession of Chattian–Late Aquitanian siliciclastic turbidites topped by the Vicchio Fm., characterized by Late Aquitanian–Early Serravallian slope deposits. The siliciclastic turbidites are interbedded with debris flows and carbonate turbidites supplied from the Apennine front and the forebulge, respectively. The foredeep basin in which the Monte Falterona Fm. deposited also includes the Macigno Fm. (Tuscan Nappe), that sedimented along the westernmost edge of the basin until its underthrusting and the subsequent migration of the fold-and-thrust system toward E. At this stage, the Monte Falterona Fm. has been transferred close to the fold-and-thrust front, triggering the sedimentation of the debris flows. In the Late Aquitanian, the Monte Falterona Fm. has been accreted to the wedge and the sedimentation of the Vicchio Fm. started. Graphical abstract

Landslides and ground deformations pose significant risks to infrastructure safety, leading to road damage, economic losses, and potential hazards for transportation networks. There are visible deformations in the section of the Tokat-Almus road between the village of Bakımlı and the town of Almus. In this part of the road, the road has narrowed due to landslides, and subsidence has occurred in some sections. To analyze and quantify these deformations, the persistent scatterer interferometric synthetic aperture radar (PS-InSAR) method was applied. Both ascending and descending synthetic aperture radar (SAR) images covering the same period (30 images between February 2022 and February 2023) were analyzed. Thus, the annual movements in the line of sight (LOS) were obtained. Considering the annual velocities in the LOS direction and given the PS-InSAR analysis results, the highest annual subsidence value was found to be 22.9 mm and the highest annual uplift value to be 17.2 mm. Standard deviation values for these velocities were determined to range between 0.7 and 10.5 mm. Decomposition was carried out to examine LOS results in detail. Examining the annual velocities in the vertical direction, the highest annual subsidence value was found to be 37 mm and the highest annual uplift value to be 23 mm. Considering the annual velocities in the horizontal direction, the annual velocity rate was found to range between −19 and 24 mm. The surface movements determined in this study provide significant contributions to the planning of engineering measures, especially in landslide risk areas such as the Tokat-Almus road. The results of the study provide valuable inferences in terms of road safety and disaster management by demonstrating the effectiveness of the PS-InSAR method in long-term deformation monitoring processes. The obtained data can be guiding not only for the improvement of highway routes and taking structural measures in landslide risk areas, but also for the development of disaster management strategies.

Coastal erosion, driven by natural factors and human activities, is a major threat to vulnerable regions like Narrabeen, Australia. This study investigates shoreline changes, berm crest elevation variations, and horizontal berm crest positions under non-storm conditions. Using a decision tree algorithm, key features influencing these phenomena were identified. For shoreline changes, berm width changes (∆BW), berm slope, sea level rise (SLR), and wave breaking index (ζ) were critical. Berm crest elevation was linked to BC height, ∆xShoreline, ∆xBC, and wave power (P), while horizontal berm crest positions were influenced by BW, berm slope, ∆yBC, BC height, wave energy (E), SLR, and ζ. The feedforward neural network (FNN) algorithm was then applied to predict these objectives. Shoreline changes were predicted with a root mean squared error (RMSE) of 3.3 m and R 2 of 92% (DS4 scenario). Berm crest elevation predictions achieved an RMSE of 0.35 m and R 2 of 75% (DY4 scenario), while horizontal berm crest positions reached an RMSE of 9.28 m and R 2 of 85.8% (DX7 scenario). These results demonstrate that parameter classification via decision trees enhances neural network predictions. The FNN proved to be a reliable tool for forecasting coastal dynamics, supporting effective monitoring and management strategies. Graphical abstract

Earth observation (EO) data cubes have gained significance for their potential in providing comprehensive insights into dynamic earth phenomena. However, a significant challenge in their utilization is the interoperability among EO data cubes with varying dimensionalities. Existing efforts have primarily focused on enhancing analysis ready data cubes, yet cross-dimensional adaptivity remains relatively unexplored. This study proposes a novel approach to address this issue. The proposed method is based on a 3D adaptive data cube model constructed in a 3D adaptive space. The main objective is to achieve interoperability between EO data cubes of different dimensions while adhering to common geospatial standards. The core methodology involves cross-dimensional mapping, considering a 3D adaptive space containing spatial axes and adaptive axes. Importantly, the method applies to data cubes with the same coordinate reference system, resolution, and data type. To enable cross-dimensional mapping, a key requirement is the existence of a mapping function between the multidimensional space domain and the 3D adaptive space domain. When these conditions are met, data cubes with three or more dimensions can be interoperable. This becomes feasible by applying suitable serialization algorithms. This study demonstrates that data cubes can be successfully mapped into the proposed 3D adaptive data cube, achieving interoperability under specific conditions. This finding has significant implications in the field of EO data analysis, enabling seamless interaction between data cubes with different dimensions. The method not only facilitates cross-dimensional adaptability but also aligns with mainstream geospatial service standards. It is particularly suitable for multidimensional geospatial raster services based on the OGC WCS 2.0 standard. In conclusion, this study addresses a critical challenge in EO data analysis by proposing a 3D adaptive data cube model that promotes interoperability between data cubes of varying dimensions.

The Daxing’anling region in Inner Mongolia has always been the most active area of tectonic magmatic activity in the Xingmeng orogenic belt. This study investigated the rock geochemistry of trachyandesite and rhyolite tuff of the Late Carboniferous Gegen Aobao Formation in eastern Inner Mongolia. This study presents new petrography, zircon U-Pb age, and whole-rock geochemical data for the Late Carboniferous Gegenaobao Formation in volcanic rocks in order to constrain their petrogenesis and geodynamic setting. The results indicate that the aluminum content of trachyandesite is relatively high, and the calcium and magnesium content is higher than that of rhyolite tuff, showing a sodium-rich characteristic. It is a quasi-aluminum peraluminous rock, and the europium anomaly is not obvious. The formation age is 304.4 ± 2.3 Ma. The calcium and magnesium content of the rhyolite tuff is relatively low, exhibiting characteristics of calcium alkali and weak peraluminous rocks. It has more obvious characteristics of light and heavy rare earth fractionation and negative europium anomalies, with a formation age of 307.6 ± 2.0 Ma. Comprehensive analysis shows that the magma of Late Carboniferous volcanic rocks in eastern Inner Mongolia mainly originates from the crust, with a deeper source of andesite and partial melting of the mantle material. Both are tectonic environments of continental margin arc volcanic rocks. The Xing’an Block and the Songnen Block completed collision assembly in the Early Carboniferous and were in a post-orogenic extension environment in the Late Carboniferous. The ancient Asian Ocean in the northern part of the Erlian Hegenshan Zhalantun Heihe tectonic belt had already closed in the Late Carboniferous, and the Xingmeng orogenic belt began to enter the orogenic extension stage.

The ecological and natural conditions in arid regions are very harsh, and water scarcity and land desertification are prominent features of these regions. The land cover of these areas has a significant impact on global climate change as well as on ecological conservation and building. In order to achieve rational and sustainable use of land resources, it is crucial to quickly grasp the changes in land use and habitat quality in arid areas. Therefore, based on the land cover data from 1990 to 2022, this study explores and analyses the characteristics of land use change and habitat quality evolution in Northwest China using land use dynamics, transfer matrix, and InVEST model. The results show that the main cover types in Northwest China are grassland and barren, and that the area of impervious has changed considerably during the period 1990–2022, with an area movement of 11.69%. Over the past 33 years, there have been frequent conversions between cropland, forest, grassland, and barren, during which the habitat degradation index has shown a trend of “increasing–decreasing–increasing,” and the overall spatial distribution of habitat quality has shown a distribution of “low in the northwest and high in the central part of the country.” There is a strong link between land use change and habitat quality change over the period 1990–2022, with land use shifts and habitat quality shifts generally following the same trend over the period. Regional differences in natural and anthropogenic factors are also more pronounced, resulting in significant spatial heterogeneity in the impacts of habitat quality in Northwestern China. As an important ecological barrier and strategic resource reserve area in the northwest region, this study is of great significance for maintaining national and even global ecological and resource security.

This article presents a study conducted at the Wadi Namar dam in southern Riyadh, Saudi Arabia, using ground-penetrating radar (GPR) to investigate underlying karst features influencing water leakage. Through the acquisition of 38 GPR profiles downstream of the dam, the investigation reveals diverse structural irregularities primarily within the Jubaila carbonate rocks beneath the dam. The processed GPR data delineated vertical fractures dissecting the carbonate bedrock, as well as parallel sheet-like lateral fracture zones. These features enhance groundwater permeability, facilitating both vertical and lateral flow, thus impacting water leakage from the dam. Additionally, the presence of dissolution and karstic cavity structures is indicated by high-resolution GPR reflections, potentially acting as underground water reservoirs. The findings underscore the significance of karst features in governing water seepage at the dam site, emphasizing the importance of geological and geophysical investigations for dam safety and management.

To better address the inversion problem of Rayleigh wave dispersion data, this study proposes a modified sine cosine algorithm (MSCA). The sine cosine algorithm (SCA) is based on a combination of sine and cosine functions for optimization; however, its performance is limited by the selection of control parameters and the diversity of population evolution. To overcome these limitations, this study introduces a modified algorithm that incorporates an exponential update strategy and a novel offspring update strategy. First, the optimization performance of the original and the modified algorithm was validated through tests on ten complex benchmark functions. Then, the MSCA, the SCA, and particle swarm optimization (PSO) were applied to the inversion of fundamental and higher-mode dispersion curves, designed under different geological conditions with and without noise contamination, demonstrating the effectiveness and reliability of MSCA in dispersion curve inversion. Finally, the use of measured microtremor dispersion data from Hangzhou, China, further showed that, compared to SCA and PSO, MSCA not only achieved smaller fitting errors and better matching with well-logging data, but also exhibited greater stability, indicating its superiority in surface wave dispersion data inversion and its potential for solving other geophysical inversion problems.

Tight sandstone reservoirs, characterized by low porosity and permeability, have long posed significant challenges for oil and gas exploration and development. This study focuses on the reservoirs in the Chang 6–Chang 8 section of the Yanchang Formation, located in the Heshui area of the Ordos Basin. These reservoirs, with their complex pore structures and diverse genesis, represent typical examples of tight sandstone reservoirs. The primary aim of this study is to systematically investigate the pore structures of these reservoirs, elucidating the mechanisms that govern permeability and thereby providing a scientific basis and technical support for the efficient development of tight sandstone oil and gas resources. Utilizing high-pressure mercury injection and nuclear magnetic resonance techniques, the study emphasizes the crucial role of pore fractal properties in controlling reservoir permeability. The results indicate that the complexity of pore structures directly influences both permeability and mobile fluid saturation. Specifically, smaller pores exhibit lower fractal dimensions, while larger pores show higher fractal dimensions, with significant impacts on both permeability and mobile fluid saturation. Additionally, the study examines the effects of different sedimentary facies and diagenetic stages on the pore structures of sandstone reservoirs. The findings reveal distinct evolutionary patterns of pore structures across various depositional environments within the braided river delta system. Furthermore, diagenetic processes and cementation play essential roles in shaping the diversity of pore structures in these reservoirs. In conclusion, this study offers new insights into the relationship between pore structure and permeability in tight sandstone reservoirs, providing valuable theoretical guidance for the exploration and development of oil and gas resources.

Catastrophic debris flows have the potential to devastate structures, infrastructure, and inflict severe economic losses and casualties. Situated within the Xianshuihe fault zone, Yusitong Gully has a watershed area of 25.88 km 2 , with a main channel length of 9.18 km and a gully bed gradient of 310‰. The combination of rainstorms and subsequent runoff makes the formation of debris flow all too common, posing a threat to both Yusitong Village and the Kangding-Mugetso highway on the alluvial fan. This study encompasses a comprehensive investigation of field conditions, calculations of essential parameters, and dynamic process simulations to better understand debris flows in Yusitong Gully. By estimating the magnitudes of debris flow within return periods of once-in-a-century, once-in-fifty-years, and once-in-twenty-years, including the peak discharge and total volume of a single debris flow event, we can accurately assess the potential risks. Additionally, a numerical technique is employed to simulate debris flow occurrences at different frequencies within Yusitong Gully, considering scenarios both with and without implemented control measures. The results demonstrate that the highest hazard level is located at the top of the alluvial fan, but significantly decreases after the execution of control measures. This study serves as a reliable scientific foundation for analyzing hazards and formulating effective mitigation plans, not only for Yusitong Gully but also for other mountainous catchments.

In order to accurately identify and evaluate the interference caused to vegetation environment during the construction process of current power transmission and transformation projects in hilly areas, especially in response to the obvious shortcomings of traditional unmanned aerial vehicles and manual detection methods, this study analyzes the composition of vegetation disturbance environment of power transmission and transformation projects in hilly areas from two aspects of natural environmental factors and human environmental factors. Taking the ±800 kV UHVDC transmission project in a certain area as an example, the multi temporal remote sensing data of the project area are collected by Landsat satellite. Combining the deep learning model with the multi model hierarchical classification algorithm, the remote sensing prediction map based on deep learning is used as the classification base map, and the normalized difference vegetation index threshold is used to classify the uncovered area. Combined with the non-building and water area in the classification base map, the candidate disturbance area composed of the two is obtained. The disturbance area is identified in the candidate disturbance area according to the size of the average reflectivity. In the disturbance area, the vegetation restoration community is analyzed according to the difference of normalized difference vegetation index in remote sensing images of different time phases. The experimental results show that the difference between the intelligently identified disturbance area and the engineering design disturbance area can be controlled within 2%, and the vegetation restoration at different time points in the disturbance area can be effectively analyzed.

The radial drainage network, which has a typical spatial distribution pattern, is a crucial component for reflecting regional geographical landscapes. Identification of radial drainage networks mainly involves the extraction of drainage segments that constitute the entire drainage network, which is conducive to enhancing the existing data and obtaining map knowledge. Previous studies on the spatial distribution of drainage networks are primarily focused on pattern identification and generalization for single-connection drainage networks. However, radial drainage networks, which normally contain multiple local drainage networks, have typical regional and combinatorial features. Thus, identifying them using available methods is challenging. In this study, a method for identifying radial drainage networks is proposed by using dual constraints on topography and geometry. First, mountain peaks that satisfy the distribution criteria are identified from the window and contour features. Then, various parameters are designed to distinguish the features of drainage segments and basins, and the components of radial drainage networks are determined. Finally, through topological operation and the distance metric, the boundary segments of the radial drainage networks are divided into different groups, and the corresponding radial drainage networks are identified. The test results demonstrate that compared with manual identification, the proposed method has achieved over 90% identification accuracy, recall rate, and F1 value.

The Qunji porphyry copper (Cu) deposit, located within the Awulale metallogenic belt (AMB) in the western Tianshan orogenic belt, exhibits distinct geochemical and mineralogical characteristics that have not been fully understood. Despite its contribution to our understanding of Cu mineralization mechanisms in reductive metallogenic systems, the oxygen fugacity and ore-forming potential of this deposit are lower than those of typical porphyry Cu deposits. Therefore, there is a need to further investigate the unique features of this deposit. The primary objective of this study was to analyze minerals and trace elements in zircon to quantify the oxidation states of the magma and investigate the presence of reduced porphyry-type magmas and ore-forming fluids in the western AMB. Through this research, we aimed to provide insights into the metallogenic mechanisms and contribute to the understanding of similar deposits in reductive environments. The zircon grains from the Qunji albite porphyry have a Ce 4+ /Ce 3+ ratio of 37.0, indicating that the deposit likely formed in a reduced magmatic–hydrothermal system, which is common in the western Tianshan orogenic belt. Our analytical methods included the use of a cathodoluminescence scanning electron microscope and laser ablation multi-collector inductively coupled plasma mass spectrometry (LA-MC-ICP-MS) to analyze trace elements in situ . The results revealed a distinct enrichment of heavy rare earth elements and a relative deficit of light rare earth elements, with a positive Eu anomaly. The zircon crystallization environment was determined to be a continental crust environment. Additionally, the Qunji albite porphyry exhibited an A-type granite geochemical affinity and was formed within an extensional environment following tectonic collisions. The oxygen fugacity of the Qunji albite porphyry was notably low, which is a significant characteristic distinguishing it from typical porphyry Cu deposits. Consequently, the Qunji Cu deposit qualifies as a reduced porphyry Cu deposit with relatively low ore-forming potential.

The characteristics of pores and fractures, as well as the diagenetic evolution path, are of great significance for identifying the formation of shale reservoirs and the enrichment mechanism of unconventional hydrocarbons. Compared with mature marine shale, medium-maturity marine shale has the characteristics of high clay content (>30%), high carbonate content (>15%), and low quartz content (<40%). The thermal simulation experiment based on medium-maturity marine shale provides a theoretical basis for understanding the relationship between pore fracture genesis characteristics and diagenetic evolution. Low-temperature N 2 adsorption, field emission scanning electron microscopy, and micro-X-ray fluorescence experiments were conducted on marine shale samples of different maturity levels obtained after thermal simulation. The results indicate that (1) When R o < 1.2%, the pore type is inorganic pores, and the fracture type is bedding fractures. The size of inorganic pores rapidly decreases under compaction. (2) When R o is between 1.2 and 2.0%, the cementation and dissolution dominate the development of inorganic pores. Micro-fractures are formed at the edge of organic matter (OM), while the bedding fractures are enlarged. A large number of dissolution pores are formed inside mineral particles, and the development of inorganic pores is highest during this stage. (3) When R o exceeds 2.1%, the hydrocarbon generation process dominates the formation and maintenance of a large number of OM pores. The framework composed of microcrystalline quartz is beneficial for the preservation of OM pores. When R o reaches 2.80%, the interparticle pores between organic matter and rigid mineral particles are further increased.

Aswan broadband seismic network with highly sensitive sensors and good station coverage gave the opportunity to study the seismicity distribution, focal depth, the fault plane solution, the attenuation of seismic wave, the station sites response, and the source spectra of Aswan earthquakes with magnitude ( M L ) \left({M}_{{\rm{L}}}) between 0.8 and 4.2 recorded from 2010 to 2023 comprehensively. Preliminary analysis of Aswan seismicity during the studied period indicated strike-slip mechanism with minor normal sense is dominant, relatively deep seismicity concentrated beneath Gabal Marawa, whereas shallow seismicity are dominant features of other zones in Aswan region, and the epicenter distribution characterized by cluster forms, frequently occurred earthquakes in the same patches, and concentrated in the intersection area between the two orthogonal fault systems. A generalized inversion technique (GIT) constrained by reference site is applied to separate the path effect, the recording station sites responses and the source spectra from the observed P- and S-waves by means of iterative least square analysis. The separated station sites effects show similar trend using P- and S-waves, with flat curve in the low frequency band from 0.8 to 8 Hz, whereas the station sites responses have peak amplitudes deviated from 4 to 10 in the higher frequencies. The attenuation through propagation is evaluated and empirically formulated Q p = ( 133 ± 2.09 ) f ( 0.54 ± 0.034 ) {{Q}}_{{\rm{p}}}\left=(133\left\pm 2.09){{f}}^{(0.54\left\pm 0.034)} and Q s = ( 91 ± 1.9 ) f ( 0.8 ± 0.045 ) {{Q}}_{{\rm{s}}}\left=(91\left\pm 1.9){{f}}^{(0.8\left\pm 0.045)} for P- and S-waves, respectively. The given attenuation fitting relation for P- and S-waves indicated the frequency dependence of seismic wave’s attenuation in the study area. The low-quality factor Q 0 {{Q}}_{0} at reference frequency ( f 0 {f}_{0} ) pointed that Aswan region is an active region. Furthermore, the low value of Q 0 {{Q}}_{0} would indicate that the medium is complex and highly heterogeneous. The third element separated from the observed seismogram is the displacement source spectra which modulated using Brune’s omega square. The advanced earthquake’s source parameters (seismic moment, corner frequency, moment magnitude, and static stress drop and source radius) and its scaling relations are computed using the converted windows of P- and S-waves. The displacement source spectra for seismic events with M L < 3 {M}_{{\rm{L}}}\lt 3 decayed rapidly at 20 Hz in the frequency bands of 0.8–50 Hz, whereas it is decreased rapidly at 10 Hz for seismic events with magnitude 3 ≤ M L < 4.2 3\le {M}_{{\rm{L}}}\lt 4.2 . The seismic moment, the source radius, and the corner frequency range from 9.50 × 10 16 to 2.18 × 10 21 dyne-cm, from 28 to 190 m, and from 4 to 36 HZ, respectively. The observed stress drops for the studied earthquakes vary from 0.01 to 12 Mpa, whereas the stress drop for S-portion ∆ σ S {\triangle \sigma }^{{\rm{S}}} ranged from 0.03 to 22 Mpa. The low stress drop values may reflect that reservoir–triggered earthquakes may have a lower stress drop than tectonic and crustal ones.

This study investigates the source, environmental impact, and health risks of potentially toxic metal(loid)s (PTMs) in agricultural soil from the Al Lith area along the Red Sea coast in Saudi Arabia. Soil samples were collected from 25 farms and analyzed using inductively coupled plasma–atomic emission spectrometry. Various indices were used to assess the contamination of the samples. Most soil samples belonged to Entisols, specifically Torripsamment, Torrifluvent, and Udipsamment subtypes. It was found that the average concentrations of PTMs follow the order of Zn (43.69) > Cu (21.69) > Ni (18.92) > Co (10.12) > Pb (2.62) > As (1.67). The average values of contamination indices indicated minor enrichment, low contamination, and low risk for all PTMs, with minor enrichment observed in some individual samples. The higher levels of PTMs in soil samples were reported from areas surrounding the Wadi Al Lith mouth. Multivariate statistics suggested that the primary source of PTMs in Al Lith soil was geogenic, derived from the weathering of basement rocks of the Arabian Shield. The mean hazard index (HI) values for humans (adults and children) followed the order of As > Ni > Pb > Cu > Co > Zn. The collective HI for PTMs in the Al Lith soil stayed below 1.0, indicating a non-carcinogenic risk. Additionally, lifetime cancer risk values for humans were lower than 1 × 10 −4 , indicating an acceptable or tolerable level of carcinogenic risk and suggesting negligible health hazards.

To make sure that regional reconstruction goes smoothly, it is important to know how rural construction areas in a river basin change over time and space and what factors affect those changes. This study focuses on the rural construction areas in the Nanxi River Basin. Through geographic information systems’ spatial analysis methods, the construction area morphology, center of gravity migration, and agglomeration degree are analyzed to reveal its spatiotemporal evolution from 1990 to 2020. The geographical detector is used to explore the interaction of multidimensional driving factors such as natural geography, socio-economic development, and cultural heritage protection. The research results show that (1) the rural construction area in the Nanxi River Basin shows an evolution trend of “agglomeration expansion and northward shift of the center of gravity.” (2) Cultural, economic, and natural factors all play a part in how rural construction areas change over time. Cultural factors, like the distance between farms and the layout of educational resources, have the most significant impact, followed by economic and natural factors. (3) The study also suggests a “cultural gene-natural base-economic potential” model that can help us understand how to protect cultural heritage and boost the economy at the same time. This result has direct guiding significance for the implementation of China’s rural revitalization strategy. It can give natural resource planning departments a scientific way to figure out the best way to use land and give cultural heritage management agencies a way to come up with safe development plans. It also provides a reference for the sustainable development path of resource-rich villages around the world. Graphical Abstract

Remote sensing images contain complex scene information and have multi-scale characteristics after being imaged by remote sensing equipment because the target instances in natural scenes are different sizes. In addition, small target instances are more difficult to identify in complex backgrounds, resulting in serious scale imbalance problems, which pose a serious challenge to identifying and positioning target objects. To address this problem, this article proposes a detection method for multi-scale remote sensing image targets by combining the frequency attention mechanism. The model first introduces an improved frequency channel attention mechanism to design a feature extraction module to improve the extraction of key features by the neural network; second, considering that the complete intersection over union method does not comprehensively consider the aspect ratio of the bounding box, which will cause the loss of small-scale target feature information, the efficient intersection over union method is used to improve it; then, because of the high missed detection rate of the non-maximum suppression (NMS) method, Soft-EIoU-NMS is used to replace the original NMS method. The experiment first conducted ablation experiments on the LEVIR dataset, where the target scale changes little and the number of ground object categories is small, and the DIOR dataset, where the target scale changes greatly and the number of ground object categories is large. The mAP@0.5 on the LEVIR dataset reached 0.935; the mAP@0.5 on the DIOR dataset reached 0.882. Then, the model proposed in this article was compared with the mainstream target detection methods. The experimental results verified the effectiveness of the model in this article. Finally, the model was applied to the disaster remote sensing image scene for detection, again demonstrating the model’s good detection performance. Therefore, the experimental results show that the method proposed in this article can effectively alleviate the scale imbalance problem and achieve a good target detection effect.

Investigations into the complex pore–throat structures (PTS) of tight sandstone reservoirs are vital for optimizing waterflooding strategies. In this study, 13 cores from the Dingbian area of the Ordos Basin were examined using casting thin sections, X-ray diffraction, high-pressure mercury injection (HPMI), and nuclear magnetic resonance (NMR). These analyses quantitatively characterized mineral composition, petrophysical properties, PTS heterogeneity, and fluid mobility. Based on these findings, high-temperature and high-pressure waterflooding experiments were performed to explore dynamic waterflooding behavior and systematic factors influencing fluid mobility and waterflooding efficiency ( E w ). Results indicate significant differences in fluid mobility across four PTS types, transitioning from uniform displacement (type I) to finger displacement (type IV), with corresponding reductions in movable fluid saturation and E w . Residual oil morphologies evolve from dispersed membranes to continuous clusters and sheet shapes. Integrated HPMI and NMR analysis accurately delineates the full pore–throat size distribution (PSD) (0.0001–10 μm) that exhibit multi-fractal characteristics, enabling differentiation of macropores, mesopores, micropores, and nanopores. Micropores, which are relatively uniform, dominate reservoir petrophysical properties and serve as primary sites for movable fluids. Elevated clay mineral content augments PTS heterogeneity, lowers permeability, and promotes nonlinear seepage phenomena (blockage and bypassing), leading to more extensive residual oil accumulation and reduced E w . Overall, PTS heterogeneity emerges as the principal factor governing fluid mobility and E w , highlighting the importance of full PSD characterization and real seepage condition simulation in reservoir engineering. This study provides key insights into optimizing water injection strategies, suggesting that maintain a stable injection pressure and injection volume multiple of less than 2 pore volumes, while also considering clay mineral sensitivity.

This study provides a comprehensive assessment of environmental and human health risks associated with potentially toxic elements in the coastal sediments of the Gulf of Aqaba. A total of 33 sediment samples were analyzed using inductively coupled plasma mass spectrometry, revealing Fe (1,526–5,123 mg/kg), Zn (16.8–32.0 mg/kg), Pb (3.5–9.1 mg/kg), Co (2.2–6.4 mg/kg), and Cd (0.05–0.18 mg/kg). The concentrations detected were within acceptable limits and below the Interim Sediment Quality Guidelines, indicating minimal environmental risk. Environmental indices, including the pollution index, modified contamination degree, pollution load index, hazard index, and lifetime cancer risk indicate no contamination or health risks for adults or children through ingestion and dermal contact pathways. Principal component and correlation analysis suggest that Fe, Zn, and Co primarily originate from natural geological processes due to their strong association with elements typically derived from bedrock weathering, while minor anthropogenic contributions may arise from tourism and coastal activities. The findings confirm that the sediments pose no environmental or health risks, providing a baseline for future monitoring and pollution management in the Gulf of Aqaba.

Considering that the traditional Hoek–Brown model only accounts for strain hardening effects in rock materials, while many rock materials exhibit strain softening effects under large deformation, a modified Hoek–Brown model has been developed to simultaneously describe both material hardening and softening characteristics. This enhancement builds upon the traditional Hoek–Brown model by introducing plastic internal variables that characterize material damage or degradation. To address numerical singularities and convergence difficulties encountered during the implementation of the modified Hoek–Brown model, a function smoothing method is employed. The physical significance of model parameters in the modified model is clarified through theoretical analysis and single-factor variable analysis methods. Finally, the modified Hoek–Brown model is applied to practical engineering calculations. The study results demonstrate that the modified Hoek–Brown model can effectively account for both strain hardening and strain softening effects in materials. The function smoothing method proves to be effective in mitigating numerical singularities and convergence issues encountered in the implementation of the modified Hoek–Brown model. For soft rock tunnels, when significant displacements occur in the surrounding rock, both displacements and stresses around the tunnel calculated using the modified Hoek–Brown model are more consistent with engineering reality than those obtained using the traditional Hoek–Brown model. It is recommended to consider applying the modified Hoek–Brown model in practical engineering calculations.

Rapid urbanization in southern Saudi Arabia necessitates accurate geotechnical assessments to support sustainable development. However, the region lacks comprehensive seismic-based soil classification studies, raising concerns regarding construction safety. This study utilizes seismic refraction and tomography techniques to characterize soil properties and evaluate their appropriateness for infrastructure development. Ten seismic refraction and tomography surveys and two multichannel analysis of surface wave profiles were conducted to classify soil conditions based on seismic velocities and geotechnical parameters. The results reveal three distinct subsurface layers: (1) alluvial soil ( V p = 300–940 m/s), (2) fractured basement ( V p = 1,350–1,890 m/s), and (3) massive basement ( V p > 2,400 m/s). According to National Earthquake Hazards Reduction Program criteria, soil classification identifies loose sediments (Class E) as unsuitable for construction, while stiffer deposits (Classes D and C) offer stable foundations. Integrating seismic techniques presents a cost-effective alternative to traditional drilling, providing a rapid assessment tool for urban planners. These findings aid in optimizing construction site selection, thereby minimizing risks associated with weak soil zones.

The United Nations sustainable development goals aim to promote global industrialization and innovation and achieve sustainable and inclusive economic development by 2030. The sustainable growth of provincial economies has emerged as a critical component of the national plan as China’s economy transitions to a new normal. This article builds an assessment index system encompassing the four "economy–society–ecology–innovation" dimensions to better assess the degree of sustainable economic development in China’s regions. It then uses the gray correlation analysis method to measure and analyze the sustainable economic development of China’s regions from 2006 to 2017. Additionally, the spatial evolution characteristics of each region’s sustainable economic development and spatial pattern are investigated through exploratory spatial data analysis and GIS spatial analysis techniques. This essay seeks to support the region’s economy in achieving sustainable development by offering theoretical underpinnings and useful advice for the creation of successful regional development plans. The study’s findings indicate that in 2017, the assessed value of the level of economic sustainability of China’s regions averaged 0.7156, an increase of 7.3% compared to 2006 while the degree of sustainable economic development in the rest of China, except Inner Mongolia, Shaanxi, and Xinjiang, has steadily increased between 2006 and 2017, the gap between the regions has also widened, with the eastern region having a much higher level of sustainable economic development than the central and western regions. An analysis of the spatial evolution reveals that the degree of sustainable economic development in every region of China generally increases in positive spatial correlation. Its spatial agglomeration is becoming increasingly apparent, with more regions becoming high-high-type and low-low-type agglomerations. Regional wealth disparities, ecological governance capabilities, and technological innovation levels were identified at the local level as essential factors for provincial economic sustainable development, with varying impacts across different regions.

Digital terrain model (DTM) has wide-ranging applications in numerous fields, including natural resource management, urban planning, environmental protection, and disaster monitoring. Utilizing LiDAR data to generate DTM is now a mainstream method. In current applications, LiDAR data are still treated as having primarily additive errors; however, studies have shown that it is affected by both additive and multiplicative errors. From the perspective of error theory and surveying adjustment, it is theoretically inappropriate to treat mixed additive and multiplicative errors directly as additive errors, as each error model is based on a distinct theoretical framework. In view of this, we applied the mixed additive and multiplicative error theory to the generation of LiDAR-derived DTM products and validated its accuracy through two real measurement cases and one simulation case. The experimental results demonstrate that the mixed additive and multiplicative errors theory provides higher accuracy than the additive error theory in both DTM fitting and interpolation. This confirms that incorporating the mixed additive and multiplicative error theory into DTM product generation is beneficial.

Background One of the best-studied subjects in the area is gold mineralization, and researchers have focused on the veins, their direct weathering products, or fluid inclusion analyses. Abu Khusheiba gold deposits in Jordan were characterized as epithermal deposits, and the wadi sediments below were investigated in some studies. Although epithermal mineralization is confirmed at Wadi Abu Khusheiba, part of the southern Jordanian Aheimer Volcanic Suite, the source of mineralizing fluids is uncertain. Among the most critical scientific issues is whether the fluids are magmatic, meteoric, or mixed, and what it would mean for gold deposition and resource potential. Moreover, the area lacks comprehensive geochemical and isotopic studies that would reveal fluid evolution, styles of alteration, and metal transport mechanisms. Stable isotope research (e.g., oxygen) in conjunction with mineralogical studies can shed light on mineralization sources and environments – addressing an important knowledge gap. Economically, fluid evolution knowledge is required to evaluate the future gold exploration potential of the Abu Khusheiba area. Results In accordance with stable isotope data (δ¹⁸O: 10.8 to 16.4‰), the fluids have a magmatic source with negligible meteoric water interaction. The evidence for a low-sulfidation epithermal system with constant state hydrothermal conditions comes from banded colloform quartz textures and potassic alteration. Such evidence enhances regional mineral exploration strategies as well as new ore-forming process understanding.

Underground coal mining impacts surface deformation not only during mining operations but also after mine closures, potentially causing prolonged subsidence or uplift. Long-term predictions of surface deformation in closed mining areas remain challenging, as traditional statistical methods have limited capability in modeling complex spatiotemporal deformation patterns. To address this, this study utilized the small base line subsets interferometric synthetic aperture radar technique to derive deformation results in the line-of-sight direction for the study area. A long short-term memory (LSTM) neural network combined with the gray wolf optimizer (GWO) algorithm was introduced to improve prediction accuracy. Experimental results showed that the LSTM model achieved a correlation coefficient of 0.93 with the observed data, while the GWO–LSTM model further enhanced prediction accuracy with a correlation coefficient of 0.99, demonstrating its potential for complex deformation prediction. The findings provide a scientific basis for surface deformation prediction and risk assessment in closed mining areas and have practical significance for disaster warning and decision-making in the region. As synthetic aperture radar data availability continue to grow, this method holds promise for broader applications in other geological environments.

Timely acquisition of geological structure information is crucial for large-scale water conservancy and hydropower projects. Understanding surrounding rock structure is critical for rock deformation analysis and geological evaluation of hydraulic adits. This study proposes a new method of OC-AINet to achieve efficient and accurate recognition of geological structure information of large hydraulic adits. An ordinary digital camera is used to obtain images of the Heishanxia adit, and dense point clouds are generated through multi-view stereo reconstruction. 2D–3D feature semantic super-pixel blocks are segmented by combining color, texture, and other geological semantics with AINet, and then a hierarchical regional clustering is carried out for rock structural planes extraction. The experimental results show that the difference between the rock structural plane identification error of the proposed method and that of the traditional manual method is less than 5 degrees, which meets the requirements of geological analysis. In addition, it has higher efficiency and safety than the manual method. The proposed OC-AINet has significantly improved the accuracy and reliability of the segmentation results and promotes the application of photogrammetry technology in geological scenes, especially in hydraulic adit environments.

Eutrophication of water bodies is a major ecological problem associated with increased input of nitrogen and phosphorus during rapid economic development. The abundance of chlorophyll-a (Chl-a) in aquatic systems serves as a reliable ecological indicator of lake water quality that is strongly correlated to the degree of eutrophication. Using multi-source remote sensing data – including Landsat and Sentinel-2 imagery – and employing Theil-Sen median analysis, the Mann–Kendall trend test, and Pearson correlation analysis, we comprehensively examined the spatial and temporal dynamics, as well as the driving forces, of Chl-a concentrations in medium and large lakes (lake area >10 km 2 ) across China from 1986 to 2023. The results showed that, over the past four decades, Chl-a concentration in Chinese lakes generally exhibited a declining trend, with a more pronounced decrease occurring after 2013. Significant regional disparities were observed: lakes in the southwestern region demonstrated the most pronounced declines, while those in the southern region exhibited the least. Seasonal variability in Chl-a was consistent across the country, with concentrations peaking between July and August and reaching their lowest levels from November to March. Nevertheless, the extent of seasonal variation differed regionally, being lower in western China and greater in the eastern and southern regions. Land surface temperature, water vapor content, and soil moisture showed strong positive correlations with Chl-a concentrations, while elevation and wind speed displayed negative correlations. These relationships also varied significantly in geographic subregions. Overall, the study revealed the spatiotemporal patterns and driving mechanisms of Chl-a dynamics in Chinese lakes and highlighted the spatial heterogeneity of ecological and geographical influences on Chl-a concentrations.

In the land classification problem, although the application of land data and remote sensing technology can provide a lot of data, the difference of data quality, data format, and data sources lead to the difficulty of land classification. Therefore, a land use classification method based on remote sensing image and multi-source data was proposed. The multi-structure element binary morphology is used to carry out the corrosion operation on the mutation pixels in the remote sensing image to complete the denoising. Based on this, the chaotic leapfrog algorithm is used to enhance the denoised remote sensing image. Through the fusion of multi-source feature data, the spatial information of remote sensing image is combined with the attribute information of other data sources to extract spectral and shape features and complete the classification of land use. The experimental results show that the R 2 value of the proposed method is 0.97, the MAE value is 0.09, and the Kappa coefficient remains above 0.9. This indicates that the method can effectively enhance the features of land remote sensing images through remote sensing and multi-source data fusion, and has ideal accuracy for multi-class land use classification, which can achieve accurate classification of land use.

The Saudi Arabian economy has reported an unprecedented increase in growth rates over the last few decades. This growth has significantly increased the demand for non-renewable energy, leading to environmental risks. Renewable energy and technological innovation are the most effective instruments for reducing carbon dioxide (CO 2 ) emissions, decreasing energy consumption, and enhancing energy efficiency. This study aims to explore the effects of renewable energy and technological innovation on CO 2 emissions in Saudi Arabia over the period 1990–2023. To investigate this nexus, examine the long-run relationship, and determine causal connections, we employ the Autoregressive Distributed Lag model and the Vector Error Correction Model. The findings indicate that renewable energy and technological innovation help reduce CO 2 emissions in both the short run and long run, improving environmental quality. Furthermore, the study utilizes several control variables related to information and communication, revealing that internet and mobile phone usage help lower CO 2 emissions in both the short run and long run, thereby enhancing environmental quality. Nevertheless, the use of fixed telephones does not affect CO 2 emissions. Based on the empirical results, this study provides valuable insights for policymakers to enhance environmental quality.

This study focuses on the sustainable development of agriculture and animal husbandry in ethnic minority regions of China, particularly examining the impact of green organic transformation. Given the increasingly severe global environmental issues, there is growing societal demand for sustainability in agriculture and animal husbandry. Ethnic minority regions face unique challenges and opportunities due to their distinct geographical, environmental, and cultural contexts. Therefore, it is crucial to investigate in depth the effects of green organic transformation on the sustainable development of agriculture and animal husbandry in these regions. Using panel data from 2013 to 2022, this study measures the Agriculture and Animal Husbandry Sustainable Development Index (ASDI) in eight ethnic minority provinces. It comprehensively evaluates sustainability from both overall and detailed perspectives. By constructing a panel threshold model, the study further explores the spillover effects of green organic transformation on the sustainable development of agriculture and animal husbandry in ethnic minority regions, informing targeted policies and strategies. The research indicates that green organic transformation has significantly positive effects on the sustainable development of agriculture and animal husbandry. We will innovate and extend the agricultural and animal husbandry industry chain, increase training for farmers and herdsmen, improve their feasible ability to make green organic transformation, and build an agricultural and animal husbandry development platform of “cooperatives + markets + farmers.” This supports high-quality development in agriculture and animal husbandry in ethnic minority regions. However, the study has limitations such as the time span and sample size of the data, which may constrain the generalizability of the results. Additionally, further research is needed to deepen the understanding of the specific mechanisms of green organic transformation. Future studies should expand the sample size, increase the length of time series, and integrate qualitative analysis methods to comprehensively understand the intrinsic mechanisms of green organic transformation on the sustainable development of agriculture and animal husbandry in ethnic minority regions.

Black and odorous water bodies in Hebei Province, China, are continuously attracting governmental attention. These water bodies exhibit a complex spatial distribution with multiple contributing factors; however, existing studies have not fully revealed its inherent patterns. Using publicly available data, this research employed various spatial analysis methods to thoroughly analyze the spatial distribution characteristics of black and odorous water bodies in Hebei Province and the coupling mechanism of multiple factors. The goal was to provide management suggestions for eliminating these water bodies. The study found the following: (1) black and odorous water bodies in this region exhibited a notable spatial clustering pattern, with kernel density results indicating concentration in the Cangzhou–Baoding–Langfang triangle. (2) The primary natural factors influencing the spatial distribution of these water bodies included the number of water bodies and temperature; social factors such as population density, ammonia nitrogen emissions, drainage pipeline density, and waste treatment capacity also played important roles. (3) There were deep-rooted causes for the spatial distribution of black and odorous water bodies, suggesting that effective management of these water bodies should focus on key areas and implementing comprehensive strategies that involve policies and regulations, infrastructure development, and ecological restoration.

To study the liquefaction problem of the dam foundation of an ultra-deep overburden in the strong earthquake area, a large-scale shaking table model test is carried out based on a 500 m rockfill dam project on an ultra-deep overburden. A laminar shear box was used to simulate the seismic response of the free field dam foundation under earthquake action. Under the action of 0.1–0.8 g bidirectional ground motion, the changes in excess pore-water pressure in different positions of the ultra-deep overburden are analyzed, and the liquefaction characteristics of the ultra-deep overburden dam foundation are discussed. The results show that the excess pore-water pressure decreases first and then increases from the bottom to the top of the overburden when the PGA reaches 0.48 g . In addition, the excess pore-water pressure is small in the clay layer and begins to increase in the surface near the ground. With the increase of the PGA, the excess pore-water pressure increases at each position, and the maximum value is located at the bottom of the overburden. The excess pore-water pressure ratio is always small below the clay layer but increases above the clay layer. When the PGA reaches 0.48 g , the excess pore-water pressure ratio exceeds 0.8, and the soil liquefies. With the increase of the PGA, from the bottom to the top of the overburden, the maximum dynamic soil pressure in the horizontal direction increases first, then decreases, and then increases, while the vertical direction is just the opposite.

The increasing frequency and severity of climate change and rising sea levels pose significant challenges worldwide, including in Vietnam. Quang Nam province’s coastal region features an extensive distribution of Quaternary deposits, dating from the Early Pleistocene to the Late Holocene. These unconsolidated sediments, along with widespread undivided Quaternary formations, contribute to the area’s geological characteristics. The province’s low-lying terrain, shallow groundwater reserves, and extensive 125 km-long coastline heighten its susceptibility to environmental changes. In this study, the MIKE SHE, MIKE 21 FM, and MIKE FLOOD models were applied for calibration (1980–2000) and validation (2001–2016). Simulations were conducted using rainfall and sea-level rise data under the representative concentration pathway (RCP) 4.5 and RCP 8.5 scenarios. The results show significant issues, including saltwater intrusion, rising groundwater levels, and flooding. Under a scenario involving a level-13+ storm and flood discharge with a 10% frequency, the flooded area is estimated at 538.3 km 2 . In a scenario with reservoirs tested for flood discharge, the flooded area increases to 721.9 km 2 . When reservoirs discharge floods with a frequency of P = 10%, the affected area is 504.1 km 2 , while reservoir flood discharge following design standards results in a flooded area of 530.1 km 2 . Riverbank and coastal erosion have caused adverse effects on groundwater resources. The extent of saltwater intrusion has been increasing over time. Under the RCP 4.5 scenario, the area affected by land subsidence due to saltwater intrusion is projected to expand from 88.02 km 2 in 2020 to 90.82 km 2 in 2030 and 93.89 km 2 in 2050.

The distribution of denudation areas has brought huge risks to the exploration and development of deep buried shale gas resources. The reservoir characterization and exploration potential near denudation areas is of great significance to the evaluation of hydrocarbon favorable areas. In this study, we integrated total organic carbon content, X-ray diffraction mineralogical composition, scanning electron microscope, elemental geochemistry, and low-temperature N 2 adsorption to characterize macro-reservoir parameters and micropore structure. Based on the abundant experimental data presented above, the origin of the pay zone in the near denudation area is clarified, and the exploration potential is addressed. The vertical variation characteristics of shale reservoir parameters near the denudation area are consistent in other regions and are controlled by global sea level changes. The main production layer is Longmaxi Formation 1st Section–Wufeng Formation. Compared to commercial pay zone of other areas, gas-rich shale near the denudation area has the characteristics of high porosity and high content of carbonate minerals. The large amount of carbonate minerals provides a material basis for the development of inorganic pores (dissolution pores and intergranular pores), making the shale near the denudation area have a larger mesoporous pore volume, and thus forming a high-porosity reservoir. The shale reservoirs near the denudation area in the study area have a high content of brittle minerals, which is conducive to hydraulic fracturing. The stable structural conditions are beneficial to the preservation of shale gas. Therefore, they have good exploration and development potential.

The study aims to analyze Permian volcanic reservoirs (VResr) in the Chengdu Janyang area in the southwestern part of the Sichuan Basin through seismic prediction (SP) techniques in order to identify and predict VResr. The study integrated geological, logging and seismic data to construct a system for identifying eruption patterns and seismic characteristics of volcanic rocks (VR). Through detailed analyses of the VRs in the Chengdu Janyang area, the distribution range was determined, and the seismic phase models of VRs in different phase zones were established, and a phase-controlled quantitative study of the VResr was carried out. The Permian VRs in Southwest Sichuan are primarily classified into three phases: spatial, overflow, and sedimentary, with rock types comprising tuffs, basalts, and volcanoclastic lavas. Based on the seismic reflection characteristics, the VRs were classified into type I eruptive phases, type II eruptive phases, mobile phases, volcanic sedimentary phases, and non-volcanic normal sedimentary phases. The formation of VResr was influenced by a variety of factors, including petrography and fluid-modifying effects, among which petrography is the distribution of Permian VResr. High-quality VResr are predominantly found in the Chengdu–Jianyang–Santai area. These pore-type reservoirs have a porosity of 9–13%, with the core area reaching up to 11%, and are mainly formed by volcanoclastic lava. The reservoirs are continuously distributed in the plane and generally exceed 120 m in thickness, making this region the most promising for current volcanic oil and gas exploration. The study not only enriches the knowledge system of SP of Permian VResr in the southwestern part of the Sichuan Basin but also provides a scientific basis and direction guidance for oil and gas exploration. The experimental testing has clarified the next favorable exploration zone for VRs in the Jinyang area, offering precise technical guidance for their development.

Inversion based on remote sensing data with high spatial resolution is an important way to obtain high-precision surface temperature. The CBERS-04 infrared sensor (IRS) can provide thermal infrared data with high spatial resolution. Owing to the lack of band response curves, research on land surface temperature (LST) inversion methods based on CBERS-04 IRS data has been very limited. Considering the consistency of the thermal infrared bands of CBERS-04 and Landsat 7 in band range and central wavelength, the key parameters of inversion, such as atmospheric upward radiance brightness, atmospheric downward radiance brightness, and atmospheric transmittance, can be obtained based on the spectral response curve of Landsat 7 thermal infrared band. Three methods such as the radiative transfer equation method (RTE), the mono-window algorithm (MWA), and the single channel algorithm (SC) were used to retrieve land surface temperature in the Minqin arid area in this research. Inversion results of the MWA algorithm based on TIRS 10/Landsat 8 and the measured land surface temperature in the same period of 2020 were utilized to verify the reliability of the inversion method. The results showed that the method proposed in the research can be used to retrieve LST from thermal infrared data of CBERS-04. The linear fitting correlation between the inversion results of the MWA method based on CBERS-04 IRS data and the inversion results of the MWA algorithm based on TIRS 10/Landsat8 was the best, with R 2 of 0.9541. This approach provides a practical solution for LST inversion using CBERS-04 IRS data.

This article studies the geological characteristics analysis and prospecting direction of the triangle top gold mine in the Saishiteng area, so as to realize accurate and massive gold mining in this area in the future. Taking the triangle top area of Saishiteng area located in the northern margin of Qaidam Basin from Saishiteng Mountain to Tanjianshan Mountain as the research object, the technical route of combining field investigation with indoor comprehensive research is adopted, and the observation of field geological characteristics is emphasized, and the geophysical characteristics are analyzed, and finally the characteristics of regional geological structure are obtained. The controlled source audio-frequency magnetotelluric method is used in combination with geological characteristics, and the distribution of gold deposits in the study area is determined by calculating and detecting electromagnetic anomalies, and the prospecting direction of gold deposits is clear. The test results show that the stratum structure in the study area is rich, the folds and faults are very complex, the primary colors of metals are distributed in all strata, and the elements of Au are more distributed. According to the results of electromagnetic analysis, there are magnetic anomalies in M15 and M18-1 areas, and these two prospecting target areas are clearly identified through detection.

Surrounding rock deformation prediction can provide decision-makers with future deformation information, which enhances construction safety. Aiming at the problems of nonlinearity, high dynamics, and low prediction accuracy of surrounding rock deformation, this article proposes a time series prediction method based on the sparrow search algorithm (SSA)-variational mode decomposition (VMD)-gated recurrent unit (GRU) deep learning model. First, aiming at the problem that parameters in VMD are difficult to determine, a VMD evaluation standard in the field of surrounding rock deformation is proposed, and the SSA is used to find the optimal combination of decomposition parameters under this standard. Second, the VMD with optimal parameters is used to decompose the surrounding rock deformation series into the trend and random term displacement. Finally, a GRU neural network with memory and feedback capabilities is built to predict the displacement components separately, and superimposed reconstruction is performed to obtain the final predicted values. The proposed method is applied to the prediction of peripheral rock deformation in a tunnel and compared with the traditional model. The results show that the proposed SSA-VMD-GRU model can accurately predict rock deformation, and the prediction accuracy of each displacement component is high, and the error is small. The final predicted R 2 of the two groups of deformation sequences were 0.9265 and 0.9119, root mean square error were 0.1262 and 0.1243 mm, and mean absolute error were 0.1101 and 0.1062 mm, respectively. This research provides an efficient and reliable solution for the prediction of surrounding rock deformation.

The Menghewula region in the southern Great Xing’an range is situated in the superposition area of the Paleo-Asian Oceanic metallogenic domain and the Western Pacific Metallogenic Domain. In this study, we carried out petrography, rock geochemistry, zircon U–Pb chronology and Lu–Hf isotope studies. The results show that granite porphyry and biotite adamellite belong to the weakly peraluminous calc-alkaline. Zircons from both rocks are magmatic in origin, with zircon U-Pb ages of 135.60 ± 0.29 Ma and 134.33 ± 0.28 Ma, respectively, and belong to Early Cretaceous granites. The ε Hf ( t ) of zircons in the two rocks are all positive (4.50–9.34), and they have younger two-stage model ages (595–904 Ma), which indicate that granitic magmatic material may have originated from the newly formed young crustal materials derived from the depleted mantle in Neoproterozoic. These rocks formed in a post-collisional extensional environment, and were influenced by the subduction and rollback of Paleo-Pacific plate. This study provides scientific basis for the relationship between magmatic evolution and mineralization in the region.

During a major earthquake, the hazard of active faults mainly manifests in two main aspects: seism enhancement near the fault (near-fault effect) and surface displacement. Since not all active faults will generate surface displacement, the hazard levels of active faults should be classified to achieve “scientific management of earthquake hazard.” Although previous classification methods have distinguished the hazard of active faults to some extent, these classification methods are often not based on the standard of “whether surface displacement will occur.” Therefore, these classification methods cannot be applied to avoid active faults. Based on the probabilistic seismic hazard assessment method, the maximum potential earthquake on active faults under different exceedance probabilities is calculated. According to the magnitude of the maximum potential earthquake and the possibility of surface displacement caused by earthquakes of different magnitudes, a classification approach for active faults is proposed. The classification method indicates that for civil buildings, only a portion ( T RP < 2.72 ka) of the surface displacement hazard of the Holocene faults needs to be considered. On this basis, the hazard levels of active faults in Yunnan Province, China, are classified. The division results reveal that nearly half of the Holocene active faults in Yunnan Province have a hazard level lower than level A. Therefore, buildings do not need to avoid these active faults.

It is one of the most feasible ways for the development of gas hydrate by vertical well and depressurization. In order to compare and analyze the characteristics and performance of gas hydrate with different geological structures developed by vertical well and depressurization, a hydrate development geological model based on Tough + Hydrate is established. The laws of gas and water production in the long-term development are studied. The analysis shows that the initial gas production rate of Class I hydrate is high, and then decreases. The cumulative gas production is significantly higher than that of other types of hydrate. It has the characteristics of high cumulative gas production and low water production. Good economic benefits can be obtained by depressurization production in vertical wells. The gas production rate of Class II hydrate and Class III hydrate increases first and then decreases. The production capacity is lower than that of Class I hydrate. Class II hydrate has serious water production problem, and its production capacity is the lowest. Therefore, Class II gas hydrate needs to solve the problem of water production. Class III gas hydrate can release production capacity through well pattern optimization or increasing reservoir contact area.

A number of channelized rock avalanche events triggered by the Wenchuan earthquake resulted in considerable destruction. Identifying the factors affecting the travel distances of channelized rock avalanches and predicting their travel distances are crucial for mitigating disaster impacts. Considering the advantages of machine learning in data processing, 49 channelized rock avalanche events and three machine learning methods (genetic programming (GP), support vector machine (SVM), and multilayer perceptron (MLP)) were selected for model optimization and establishment. It was found that the GP model outperforms both SVM and MLP, achieving the best fit alongside superior predictive accuracy and error control. The proposed GP model, which takes into account influences of the source area, height difference between the head scarp crown and the base of the collapsed slope, average inclination angle of the source zone, and average slope angle of the travel path, was compared against six previous models from the literatures using 15 channelized rock avalanche events induced by the Wenchuan earthquake in the Tuojiang River Basin. The proposed GP model achieved significant improvement in estimating the channelized rock avalanche travel distance. In view of the limited number of channelized rock avalanche events, the application range of the proposed GP model is suggested. In conclusion, the proposed GP model could play a beneficial role in related rock avalanche disaster prevention.

Effectively identifying tourists’ landscape preferences is significant for the conservation of heritage sites and forms the foundation for landscape assessment. This study conducted a content analysis of 4,511 photos uploaded by hikers in the Three Parallel Rivers region to explore their landscape preferences, differences between natural and cultural landscapes, and the underlying reasons for these preferences across different prefectures, in order to inform landscape management and heritage site conservation strategies. Results indicate a strong preference for natural landscapes, particularly forests, mountain peaks, blue skies, and lakes, while cultural elements such as architectural structures received significantly less attention. Preferences for landscape categories were especially high for vegetation, geomorphology, and meteorological landscapes, reflecting the region’s outstanding natural values that underpin its designation as a World Natural Heritage site. In contrast, geological sites were less frequently featured in the photos, likely due to their low visual appeal, limited public awareness, and a gap between hiker expectations and interpretive infrastructure. By providing quantitative evidence to support landscape management, spatial planning, and conservation strategies aligned with visitor behavior, this study helps heritage site managers optimize resource conservation and presentation strategies, contributing to sustainable tourism development through the balance of visitor satisfaction and heritage conservation.

During the Last Glacial Maximum (LGM), valley glaciers in the Bavarian/Bohemian Forest mountains (Central Europe) reached their maximum extent, forming prominent terminal moraines. In this study, we provide the timing for the terminal moraine complex in the south-facing Großer Schwarzbach Valley using in situ produced cosmogenic 10 Be dating. The moraine complex was deposited during the onset of the LGM, as indicated by the weighted mean exposure age of 25.7 ± 1.1 ka. This age represents the oldest cosmogenic data obtained for terminal moraine ridges in the Bavarian/Bohemian Forest. It is also older than the dated LGM moraines in other Central European Variscan ranges. In the Bavarian/Bohemian Forest, the age suggests an asynchronous stabilization of LGM moraines across the mountain range, which we attribute to differences in insolation of north- and south-facing sites. Additionally, the youngest preserved moraine, located at the mouth of the cirque, was deposited around 16.2 ± 0.6 ka.

Tectonic movement and magmatic activity are complex geological processes resulting from the interaction of multiple factors. These processes are often characterized by local singularities with nonlinear characteristics. This study drew on the Multifractal Local Singularity theory and took the Heyu granite magmatism and the Mesozoic tectonic evolution of the Xiong’er Mountain as examples to explore singularity characteristics in the process of tectonic evolution and magmatism. The aim was to enhance the understanding of geological evolution and offer new perspectives for studying geological evolution processes. The research showed that during the four stages of the Heyu granite magmatism, the early phase exhibited a low singularity index, weak magmatic activity, and relatively rapid crystallization. In the middle phase, the singularity index and eruption intensity gradually increased, whereas the crystallization rate decreased. The third stage had the highest singularity index and peak eruption intensity, with the lowest crystallization rate. In the final stage, the singularity index decreased, eruption intensity weakened, and crystallization rate increased. In the case of the Mesozoic tectonic evolution of the Xiong’er Mountain, three distinct stages were identified: early-stage compression and warming, mid-stage structural transformation, and late-stage decompression and extension. The singularity characteristics show a progressive increase from the early to the late stages, indicating a gradual intensification of tectonic activity.

In power transmission and transformation engineering, tower foundation construction activities are frequent, and their interference with the surrounding soil environment is becoming increasingly prominent. During the construction of such projects, tower foundation construction involves site excavation, foundation pouring, and backfilling operations, which can damage the original structure of the soil, change the moisture and nutrient status, and thus affect the soil ecosystem. To gain a profound understanding of the impact of engineering activities on soil environments and ensure soil ecosystem stability, this article investigated the distribution characteristics of soil moisture content and vertical microbial communities in the disturbed areas of transmission and transformation tower foundations. Soil samples were collected from the study region, and the extent of soil collapse, crack density, and soil moisture content were calculated using the dry method after post-disturbance vegetation restoration. The study determined the microbial distribution characteristics in areas with soil collapse and vegetation degradation, and analyzed the impact of varying distances from the disturbance center on microbial distribution. Experiments revealed that under the influence of tower foundation construction, areas with extreme soil subsidence and high crack density exhibited lower soil moisture content. During the construction process of the tower foundation for power transmission and transformation projects, mechanical operations such as rolling and excavation directly damage the root system of vegetation, making it difficult for plants to grow stably and hindering their absorption of water and nutrients from the soil, thereby affecting their normal physiological activities and leading to poor or even degraded vegetation growth. In areas with severe soil subsidence, the microbial populations of bacteria, actinomycetes, and disturbance of the tower foundation construction disrupt the pore system of the soil, making the soil close to the disturbed area less aerobic and permeable, and the microbial living space is compressed. The survival space of microorganisms is compressed and thus reduced. As the distance increases, the degree of soil disturbance gradually decreases, resulting in an increase in the number of microorganisms. Interference activities such as tower foundation construction in power transmission and transformation projects have damaged vegetation, resulting in vegetation degradation, significant reduction in leaf litter, and severe shortage of organic matter supply in the soil. The growth and reproduction of microbial communities are highly dependent on the energy and nutrient deficiencies provided by organic matter, leading to a decrease in their distribution.

The hydrocarbon system of the second member of the Kongdian Formation (E k 2 ) in the deep subsag zone of the Cangdong Sag, Bohai Bay Basin, has recently witnessed exploration success. However, the oil accumulation process of the E k 2 reservoir in this deep subsag zone remains poorly understood. This lack of knowledge restricts our comprehension of the hydrocarbon system in the deep subsag zone and further hydrocarbon exploration efforts. In this study, the oil accumulation process of the Kongdian reservoir in the deep subsag zone of the Cangdong Sag was investigated by examining the origin of oil, oil generation history, and timing of oil charge. The E s 3 source rocks were deposited in a freshwater, anoxic to sub-oxic environment with a mixed input of aquatic organisms and terrigenous organic matter. In contrast, the E k 2 source rocks were deposited in a hypersaline, anoxic to sub-oxic environment with a substantial input of green algae. Analyses revealed that the oil in the deep subsag zone of the Cangdong Sag originated from the E k 2 source rocks. The dominant stage of intense oil generation for the E k 2 source rocks took place from 48 to 25 Ma. This process was evidently controlled by the rapid subsidence and temperature increase of the E k 2 Formation. During this dominant oil generation stage, the maximum oil generation rate of the E k 2 source rocks reached approximately 76 mg/g * TOC * my. The major episode of oil charge in the E k 2 reservoir occurred around 45–40 Ma, indicating an early oil accumulation event in the deep subsag zone of the Cangdong Sag. Owing to the underdeveloped fracture system, the oil generated from the E k 2 source rocks in the deep subsag zone of the Cangdong Sag could charge into the adjacent E k 2 reservoir. This proximity avoided the dispersal of oil and the readjustment of oil distribution caused by long-distance migration, thereby facilitating the formation of large lithologic reservoirs.

Understanding the velocity profile of geophysical flows is essential for analyzing their dynamical characteristics. However, existing studies have predominantly concentrated on the velocity profile of debris flows and rock avalanches, neglecting the investigation of rock–ice avalanches. Thus, this work investigates the velocity profile of rock–ice avalanches in the vertically rotating drum using particle image velocimetry. In this work, findings indicate that higher ice content leads to a shift from shear flow to sliding, thereby improving the overall mobility of the avalanche. As the particle size increases, there is a transition in flow patterns from parallel to chaotic, leading to heightened turbulence and collision interactions. The rotational speed of the drum affects the overall movement speed but does not significantly affect the internal shear behavior. These findings offer theoretical and experimental insights into the dynamics of rock–ice avalanches, enhancing our comprehension of multiphase flows and their characteristics.

Wavefront (WF) construction-based ray-trace modeling is an advanced technique used to assess survey design parameters in three-dimensional (3D) seismic surveys. These parameters play a crucial role in determining the quality (signal-to-noise ratio and signal strength), coverage, and continuity of seismic data during the imaging of subsurface structures. Unlike conventional survey design, which assumes horizontal homogeneous velocity layers and straight-ray propagation, the Earth’s surface consists of inhomogeneous and complex layers. Conventional seismic survey design methods are constrained by their reliance on simplified assumptions that inadequately represent geological complexity. Their limited adaptability to structurally complex environments reduces their effectiveness in optimizing data quality and subsurface imaging. The present study provides insights into how heterogeneous layers, characterized by variations in thickness and petrophysical properties, influence seismic data. The WF construction approach assimilates elastic wave propagation through a heterogeneous and geologically complex Earth medium. This method involves constructing an elastic Earth model with inhomogeneous properties and the propagation of elastic wavefields through the model by strategically positioning sources and receivers in accordance with the designed survey geometry. Illumination (Hitmaps) and Ray hit maps of target layers are generated. The findings indicate that WF construction modeling offers a more accurate depiction of real-time illumination of target layers. In conclusion, WF construction modeling is a valuable tool for assessing the merit of designed survey parameters. It aids in finalizing geometry parameters with a logical understanding and knowing their impact on illumination for target layers.

The increasing levels of CO 2 emissions have become a major concern for scientists and policymakers worldwide. As a result, they have been exploring various potential solutions to reduce CO 2 levels, and one such solution is carbon capture and storage. Field observations, sedimentological analyses, diagenesis, and petrography were performed in a preliminary evaluation to assess the reservoir potential for geological CO 2 storage in the Phra That and Pha Daeng Formations, Lampang Group. The succession can be divided into ten lithofacies types and grouped into three distinct depositional units, corresponding to a fluvial environment. The outcrop and subsurface core samples underwent distinct diagenetic processes that markedly influenced their reservoir properties. Primary porosity in outcrop samples has been reduced by iron oxide cements, followed by calcite cements and quartz overgrowth cements. The core samples reveal a considerable compaction, iron oxide, quartz overgrowth cementation, authigenic clay, and calcite, which ultimately reduced the porosity of the potential reservoir. Thus, the Phra That Formation is not suitable as a CO 2 storage reservoir.

In order to make better use of the spatial information and spectral information to improve the classification accuracy of hyperspectral remote sensing images, this article proposes an improved classification algorithm, a-HybridSN, based on the HybridSN (hybrid spectral network) model. First, the hyperspectral remote sensing image is carried out by principal component analysis to reduce the dimensionality for eliminate spectral redundancy. Second, the channel attention mechanism and spatial attention mechanism are integrated between the flatten layer and the dropout layer of the HybridSN model, which helps alleviate the interference of inconsistent classification results caused by random sampling in the dropout layer. Finally, experiments are conducted on the images from the GF-5 and Sentinel-2. The experimental results indicate that the proposed method can effectively improve the classification accuracy and alleviate the phenomenon of salt and pepper in the classification result image. The proposed method achieved state-of-the-art performance with an overall accuracy of 89.55% and a Kappa coefficient of 0.8712. Comparative analyses demonstrate a-HybridSN’s superior performance against established methods such as HybridSN, random forest, SoftMax, decision tree, and support vector machine. These results underscore the robustness and adaptability of a-HybridSN.

Nigeria faces severe food security challenges caused by insecurity, flooding, and soil erosion. In the north, insecurity has prevented many farmers from accessing their farms, while in the south, especially in Anambra State, gully erosion is causing a severe menace not only to farmers and their farmlands but also to properties, and even social infrastructures in the state are not spared. Many research studies have been carried out on soil erosion and gullies in Anambra State, and solutions have been suggested, yet the menace continues. There are also conflicting reports from researchers about the actual number of gully erosion sites in Anambra State including disparities in the estimated soil erodibility in the state. This study analysed and mapped the susceptibility of soils to rainfall erosivity (high, medium, and low) and gully erosion sites in Anambra State. It reveals that the differences in erodibility result from sources of soil data and their spatial resolution. Climatically, the state falls within Koppen’s Af region, with the mean annual temperature and rainfall of around 27°C and 1,870 mm, respectively. The data requirements for this study were from primary and secondary sources. The primary data include field visits, observations, measurements, photographs, soil specimens, experimentation, and brain sensing. The secondary data sources include the Internet, related literature, soil data from the Food and Agriculture Organization (FAO), and satellite images. Geographic information system-based analyses were done using ArcGIS 10.4.1. The results are presented in two-dimensional topographic raster and vector overlaid maps of Anambra State, showing the geological formations, areas in the state with high-, medium-, and low-susceptibility to soil erodibility, and the existing gully erosion sites in the state.

Land use conflicts in China's south-north transition zone have become increasingly severe, posing significant challenges to regional planning and sustainable development. Taking the Huaihe River Basin as an example, based on the land use data from 1990 to 2020, this study utilized a land use conflict assessment model and the geodetector model to reveal the dynamics of these conflicts and their underlying causes. The results show that the cultivated land area in the Huaihe River basin shows a decreasing trend, while the construction land area shows a marginal expansion trend. At the same time, the degree of land use conflict decreases first and then intensifies. In addition, the spatial agglomeration effect of land use conflicts is significant, especially in the northern part of the Huaihe River basin. It is also found that elevation, slope, and population density are the main factors affecting land use conflicts in the Huaihe River Basin. These findings underscore the importance of considering the unique characteristics of the Huaihe River Basin in developing targeted land use policies that balance economic growth with environmental protection.

Assessing the impacts of land-use change on seismic risk distribution is crucial for enhancing land-use planning and earthquake mitigation strategies. This study establishes a comprehensive evaluation system integrating geographic information system technology and entropy-weighted Technique for Order Preference by Similarity to Ideal Solution methodology (incorporating 14 indicators across hazard, vulnerability, and risk dimensions) to quantify county-level earthquake risk in Sichuan Province, China, and investigates the effects of land-use changes on seismic risk patterns. Results show that (1) dominant land-use transitions involved cropland (decreasing from 24.74% to 22.76%), forest cover (+17,702 km 2 ), and impervious surfaces (+3,558 km 2 ). Landscape metrics indicated reduced patch density, diminished edge complexity, and simplified shape irregularity alongside increased spatial aggregation. (2) From 1990 to 2023, earthquake risk distribution showed strong spatial autocorrelation (Global Moran’s I = 0.58, p < 0.001), with more than 75% of the province classified as very low-risk. The very high-risk areas were mainly concentrated in the western, central, and southern regions, while the eastern region was predominantly very low-risk, covering a wide area. (3) Land use composition displayed distinct gradients across risk zones: forest expansion in very-low-risk (+4.38%) and high-risk (+28.47%) areas reflected successful Grain-for-Green policy implementation. Notable grassland fluctuations and wetland degradation highlighted ecological fragility, underscoring the urgency for risk-adaptive land management interventions. (4) As land-use intensity and landscape fragmentation decrease, the area of high earthquake risk zones declines, suggesting that scientific land-use planning and effective disaster mitigation measures can reduce regional earthquake risk. Additionally, inter-city earthquake risk in Sichuan Province exhibits significant spatial heterogeneity, with western cities forming “high-high” risky clusters and eastern cities forming “low-low” risky clusters. These results provide actionable insights for provincial-scale disaster mitigation frameworks and municipal-level prioritization. The study advances methodological innovation and theoretical foundations for regional earthquake risk assessment and sustainable land-use optimization.

This case study investigates the long-term seam performance over time for six geotextile tubes, which incorporated three distinct seam designs: longitudinal (L), transverse (T), and longitudinal with additional transverse reinforcement (L + R). These tubes, which differed in both diameter and length, were installed at the Saemangeum Test Bed site in South Korea. The observations focus on seam alignment, stitching behavior during filling, and over a decade (2013–2023) of environmental exposure. The study reveals that the T-type seam pattern ensures superior slurry distribution during the filling phase, preventing uneven tube profiles. However, for long-term stability and performance, the L + R seam pattern emerges as the most effective, benefiting from the reinforcement effect against tensile stress. The investigation further indicates the necessity for caution against foundation scour, which compromised the integrity of one tube in the study. This research contributes to the field by providing empirical evidence on the effectiveness of different seam patterns for geotextile tubes in marine environments, offering practical insights for their design and installation. By identifying the L + R seam pattern as the most viable for long-term durability, this study guides engineers in optimizing geotextile tube construction for environmental and coastal engineering projects.

Low-permeability reservoirs in the western South China Sea hold substantial hydrocarbon reserves; yet, their development via water injection remains suboptimal due to challenges such as high CO 2 content (22–42%) in associated gas. To enhance recoverable reserves and unlock residual oil potential, this study investigates the interplay of gas–water–rock interactions during CO 2 flooding under extreme reservoir conditions: high temperature (140°C), high pressure (33 MPa), and significant dip angles (5–20°). Laboratory experiments and mechanistic analyses were conducted to evaluate phase-state transitions of crude oil, CO 2 -driven permeability enhancement, and recovery efficiency. Results demonstrate that CO 2 injection induces a phase shift from liquid-dominant to gas-dominant systems, elevating the gas–oil ratio and saturation pressure while expanding the gas–liquid phase boundary. Notably, steeper reservoir dip angles synergistically amplify recovery rates, achieving a peak efficiency of 88.8 at 80% CO 2 injection. Rock dissolution, driven by CO 2 –water interactions, selectively dissolves feldspar (45% plagioclase) and clay minerals (22% kaolinite), enlarging pore radii (0.8 → 1.2 μm) and increasing permeability by 10%. Dynamic displacement experiments further reveal a 10% rise in gaseous-phase permeability post-dissolution. These findings highlight the dual role of CO 2 flooding in enhancing oil recovery and enabling carbon storage, with annual sequestration potential projected at 2.5 × 10 4 tons. The study provides a technical framework for optimizing CO 2 -EOR strategies in offshore low-permeability reservoirs, offering critical insights for sustainable hydrocarbon extraction and carbon management in analogous high-temperature, high-pressure environments.

Naphthenic acids (NAs), herein were referred to as a class of carboxylic acid mixtures, are believed to be emitted as byproducts of petroleum production and processing. Extensive research has indicated their widespread distribution in the environment, along with local ecological impacts. Due to the similar fossil power of coal and petroleum, NAs were detected in coal as well. However, few studies correlated with coal. In this study, we established a comprehensive set of extraction and analytical methods for detecting NAs in coal. By combining solid-phase extraction and ultra-performance liquid chromatography–mass spectrometry, and after conducting preliminary experiments, we selected NaOH as the primary extractant. A total of 71 distinct NAs were identified in coal samples during this experiment. Upon comparison with a petroleum-derived NA standard, significant differences between coal-derived NAs and those from petroleum were observed. Consequently, the concentrations of NAs in the samples ranged from 17.6 to 56.9 μg/g, providing evidence for the presence of a substantial amount of NAs in coal. This suggests that coal and coal gangue may be significant sources of NAs in the environment.

Improving the strength of clayey soils remains a key concern in geotechnical practice. This study presents a comparative investigation of the effects of olivine waste and sand grains on a clayey soil, both in the absence and presence of potassium hydroxide (KOH). The samples were tested under undrained conditions with varying curing periods and different confining pressures. Samples treated with sand or olivine showed increased strength, with sand outperforming olivine due to its higher volume. In the presence of KOH, the strength increased in both cases, with sand-treated samples showing higher values than those treated with olivine. Under a confining pressure of 100 kPa and a curing period of 90 days, strength increased by 399% with olivine and by 613% with sand. At 400 kPa confining pressure, these values reached 604% for olivine and 856% for sand. The highest cohesion (390 kPa) and internal friction angle (22.03°) were obtained in the sand added sample activated with KOH in 90 days, followed by the olivine added samples activated by KOH in 90 days with a cohesion value of 270 kPa and a friction angle of 20.56°. Mineralogical analyses were carried out to examine crystal formation resulting from the chemical reaction. The study presents extensive experimental data, highlighting the potential of olivine as an environmentally friendly material and a sustainable alternative for soil stabilization, with lower carbon emissions compared to conventional methods.

Forests not only provide important resources for the earth, but also play an important role in maintaining ecological balance. Once forest fires occur, they will pose a serious threat to human beings. As a convenient and high-definition remote sensing equipment, unmanned aerial vehicle provides effective support for forest fire monitoring. The early forest fire smoke flame target is small and the dynamic change is difficult to identify. In this study, a forest fire dataset is first constructed, and an early forest fire detection model, YOLO-MC, based on YOLOV8, is proposed. Lightweight backbone network MobileNetV3 is used to replace the original backbone. Convolutional block attention module attention mechanism is added to make the model pay more attention to local information connection. Bidirectional feature pyramid network and small target layer are introduced to dynamically assign weights to identify small targets. Dynamic head is used to improve the flexibility of model inspection. Generalized intersection over union is used as a loss function to improve the accuracy of target positioning. Compared with the benchmark model, the accuracy and average accuracy of YOLO-MC are increased by 4.3 and 3.6%, respectively, and the number of parameters is reduced by 2 × 10 4 , which indicates that YOLO-MC can improve the identification efficiency of early forest fires and realize the early detection of forest fires at the same time. It helps in identifying forest fires as early as possible and has certain practical significance.

This study investigates the capabilities of the proposed approach that fully utilizes the intensity, phase, and polarimetry features of Sentinel-1 for multi-class and binary classification of building damage, aiming to address the limitations of existing studies, which typically only utilize partial Sentinel-1 features or integrate them with an ancillary dataset for building damage classification. Using the Puerto Rico earthquake and Turkey earthquake as case studies, multi-class classification achieved F 1 score of 0.50/0.58, an overall accuracy of 0.52/0.60, and receiver operating characteristic – area under the ROC curve (AUC) value of 0.59/0.69, respectively. For binary classification under varying proportions of collapsed buildings and no damaged buildings, overall accuracy ranged from 0.631 to 0.813 and F 1 score from 0.630 to 0.806 across the two earthquakes. The performance for both classification tasks is comparable to previous studies that employed other synthetic aperture radar data or integrated one or two Sentinel-1 features with ancillary dataset, and the performance is better than that of studies using only one or two Sentinel-1 features for the destroyed vs undamaged classification. These quantitative comparisons highlight that our proposed approach, which solely rely on Sentinel-1, exploiting all features of Sentinel-1 data and using a readily applied machine learning model, is suitable for the timely, rapid, and microregion-level building damage detection in the local region after the earthquake.

This study examined the frequent occurrence of landslide disasters in the upper reaches of the Yellow River (from Gonghe to Xunhua) using Sentinel-1A data from January 2021 to December 2023 and integrating it with small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology to extract surface deformation rates and combine optical remote sensing imagery to detect potential landslides. The annual average ground deformation rate in the study area ranged between −139 and 73 mm/a along the ascending track, whereas it exhibited broader variation from −237 to 123 mm/a along the descending track. Through the integration of SBAS-InSAR measurements with optical remote sensing imagery, we successfully identified potential landslides within the research area. Specifically, 171 potential landslides were detected in the ascending track and 154 in the descending track, with 98 overlapping landslides recognized by both datasets. The present study primarily analyzed the influencing factors of the Guobude landslide, which exhibited the fastest deformation rate. A steep slope, low vegetation coverage, and unfavorable lithological mechanical properties were the predominant factors contributing to this landslide. In contrast, precipitation had an insignificant impact on landslide deformation. The multi-temporal InSAR approach, combined with geotechnical evaluation, offers an effective methodology for regional landslide identification and mechanism analysis in complex terrain. This study demonstrates the effectiveness of integrating SBAS-InSAR with optical remote sensing for landslide monitoring, providing a scientific foundation for disaster monitoring, risk assessment, and early warning in the upper Yellow River basin. The findings hold substantial significance for the development of real-time monitoring and early warning systems, the optimization of governance measures, and the promotion of ecological security and high-quality sustainable development in the upper reaches of the Yellow River.

Advances in remote sensing technology have facilitated the assessment of land vegetation and agricultural development using satellite images. This study’s normalized difference vegetation index (NDVI) is vital in assessing vegetation coverage and tracking land-use changes. Integrating NDVI with convolutional neural network (CNN) models and random forest (RF) improves the accuracy of cropland discovery and vegetation evaluation in Najran city. This study proposes a hybrid method for discovering Najran, Saudi Arabia's agricultural lands. The proposed method combines the strengths of CNNs, namely, MobileNetV2, GoogLeNet, and DenseNet121, with RF, NDVI, and Landsat 8 satellite images to classify and analyze terrestrial vegetation accurately. Study data from 2013 and 2022 were used to evaluate and compare cropland discovery. The study finds that green space increased from approximately 860,297,400 m² in 2013 to approximately 909,567,900 m² in 2022. Various crop categories contributed to this increase, with vegetation surging by 220,794,300 m² in 2013, followed by a decrease to 161,650,800 m² in 2022. The average green area shifted positively from 57.36% in 2010 to 58.85% in 2022, indicating an overall enhancement in green land discovery and utilization in Najran city over the past decade. The hybrid systems for classifying agricultural land areas produced a confusion matrix that reached an overall accuracy of 96.87% in 2013 to 98.89% in 2022. The Kappa coefficient, a measure of agreement, ranged from 0.945687 in 2013 to 0.973832 in 2022, demonstrating the strength and robustness of the proposed hybrid approach.

Hydraulic conductivity is a critical parameter in geotechnical and hydrogeological engineering, influencing groundwater flow, structural stability, and environmental remediation. Traditional methods for estimating hydraulic conductivity are often time-consuming and costly, relying on empirical formulas that are limited to specific soil types. This study proposes a novel approach using an artificial neural network (ANN) to predict hydraulic conductivity for both fine and coarse-grained soils based on grain size distribution. A comprehensive dataset of 205 soil samples was used to train and validate the ANN model. The model’s performance was compared with traditional methods, including empirical formulas and multiple linear regression, to achieve realistic estimation. The ANN model demonstrated superior accuracy in predicting the hydraulic conductivity with a regression coefficient ( R 2 ) exceeding 92% and a root mean squared error of 0.077. A key innovation of this study is the development of a unified prediction approach that requires only the weight percentages of soil components, making it practical and cost-effective for engineering applications. The results highlight the potential of ANN to provide accurate and efficient estimates of hydraulic conductivity, offering significant advantages over conventional methods. This research contributes to advancing the use of machine learning in geotechnical engineering and provides a robust tool for soil characterization and groundwater management.

In recent years, the classification of land change has revolutionized the ability to monitor and understand dynamic changes occurring on the Earth’s surface. Artificial intelligence (AI) techniques must improve the performance and accuracy of land change detection by extracting spectral features from several Convolutional Neural Networks (CNNs) and integrating them. In this study, AI techniques were applied to classify the land use and land cover (LULC) of the Najran city map in Saudi Arabia based on 2020 Landsat 8 satellite imagery. This was achieved using several hybrid models combining CNN and random forest (RF) models, namely AlexNet-RF and GoogLeNet-RF, as well as the combined spectral features of AlexNet-GoogLeNet with RF. The results showed that LULC classification using a hybrid system was superior to CNN and proved that the proposed hybrid system of combined spectral features extracted from AlexNet-GoogLeNet with RF provided better results than using the hybrid system proposed by AlexNet with RF and GoogLeNet with RF. The proposed hybrid system of combined spectral features extracted from AlexNet-GoogLeNet with RF achieved an accuracy of 96.95%, a Kappa coefficient of 0.9638, sensitivity of 96.95%, AUC of 98.4%, and specificity of 99.83%. The proposed hybrid methods aim to enhance the classification accuracy and increase the robustness of the system, ensuring consistent performance across diverse earth-change scenarios. It substantially impacts various domains, including environmental monitoring, disaster management, and sustainable urban planning.

This study proposes a streamlined workflow for producing historical digital elevation models (hDEMs) from scanned 1950s aerial photographs using structure-from-motion and multi-view-stereo (SfM-MVS) techniques along with co-registration methods. We also conducted a sensitivity analysis to assess the impact of DEM references with varying spatial resolutions on the SfM-MVS process and co-registration accuracy. The DEM references included a 30 m SRTM DEM (low resolution), a 12 m TanDEM-X DEM (medium resolution), and a 5 m DEM provided by the General Directorate of Mapping of the Ministry of National Defense, Republic of Türkiye (high resolution). Results indicate that higher resolution reference DEMs lead to improved accuracy and precision of the final hDEMs, particularly evident in reduced errors and finer spatial resolution. This study contributes to streamlining the hDEM construction process and offers a nuanced understanding of the significance of elevation and reference DEM selection in ensuring hDEM accuracy.

The Yangtze River Delta (YRD) is a highly urbanized region. Understanding the spatial and temporal dynamics of urban land expansion in the YRD is essential for optimizing the distribution of urban clusters and promoting balanced regional development. Existing 30 m global impervious surfaces (IS) datasets are hampered by significant accuracy discrepancies and uncertainties stemming from inconsistent methodologies. Therefore, the Dempster–Shafer (D-S) evidence theory was used to fuse three datasets of the China Land Cover Dataset, Global Land Cover product with Fine Classification System at 30 m, and Global Impervious Surface Area, to analyze the urban land expansion in the YRD from 1985 to 2020. The results indicate that: (1) The fused dataset achieved an overall accuracy of 98.26% and a kappa coefficient of 0.965, notably higher than those of the individual datasets. (2) Urban land expansion in the YRD exhibited distinct phases: slow growth, rapid growth, and rapid decline. The early expansion was more intense in the southern YRD. Later, the northern region expansion accelerated, shifting the development center northward. (3) The urban development pattern of the YRD was predominantly characterized by edge expansion, with the proportion of infilling gradually increasing. Long-term and high-precision data on IS provide a comprehensive understanding of the development patterns of regional urban clusters. The D-S evidence theory enhances the accuracy of IS dataset, serving as a foundation for planning and promoting high-quality urban development.

Creating three-dimensional (3D) geological models is important for advancing our understanding and visualization of geological structures and phenomena. This helps in formulating geological decisions, planning geological operations, and optimizing the development and management of geological resources. However, traditional computer-aided 3D geological visualization is inadequate regarding human-computer interaction and limited in its ability to represent dynamically evolving geological processes. The emergence of digital twin and metaverse technology has the potential to facilitate dynamic and process-oriented visualization of 3D geological models. We present a metaverse-based approach to the dynamic 3D characterization of geological models and processes. A set of time-series 3D geological grid models which reflect numerical simulation results are mapped and managed in the metaverse, and a simplification strategy for 3D grid models is also developed. By switching properties in the simplified 3D model in the metaverse, we form the framework for dynamically representing geological processes. The effectiveness of this approach is demonstrated by using a reservoir model; the simplification strategy reduces the reservoir model to 6.67% of its original size and lowers interaction latency to within 30 ms. This work provides a novel solution for the comprehensive application of metaverse technology in subsurface space development and geological resource exploration.

This study examines the stratigraphic architecture of the Upper Pleistocene on the Larache margin, NW Moroccan Atlantic, focusing on the impact of glacio-eustatic cycles. Utilizing seismic analysis, we identified four major seismic sequences (UD, UC, UB, UA) over the past 500,000 years, influenced predominantly by ∼100,000-year Milankovitch cycles. These sequences are delimited by significant erosional unconformities corresponding to glacial lowstands. Our analysis reveals periods of subsidence and tectonic uplift, affecting sediment deposition and progradation. By correlating seismic units with marine isotope stages, we establish a comprehensive chronostratigraphic framework. This study enhances the geological understanding of the Larache margin by detailing the interactions between tectonic activity, climate changes, and sea-level variations.

To advance the understanding of Mesoproterozoic sedimentary facies and delineate their distribution patterns in the Ordos Basin, this study systematically analyzes the sedimentary characteristics and stratigraphic architecture of the Mesoproterozoic sequences using integrated datasets, including core samples, well logs, seismic profiles, and field outcrops. The results indicate that the Mesoproterozoic Changcheng System, shaped by rift-related processes, displays a southwest-to-northeast thinning trend, culminating in stratigraphic pinch-out. Three Facies associations are identified: rift-related sandstone-shale and volcanic eruption facies (dominated by sandstones with abundant basaltic eruptives in the Changcheng rift trough), shoreface sandstone facies (primarily lower shoreface subfacies with quartz sandstones and mudstones), and fluvial facies (sandstone-siltstone sequences). During the Jixian System, post-Changcheng “compensatory sedimentation” led to a significantly reduced depositional area dominated by tidal flat environments. A southwest-to-northeast facies transition progresses from shelf facies (siltstones interbedded with shales and bioclastic limestones) through mid-ramp facies to tidal flat and mixed tidal flat facies, collectively forming a carbonate tidal flat system dominated by siliceous-banded dolomites. The Mesoproterozoic succession in the Ordos Basin thus records an evolutionary trajectory from rift basin infilling (“leveling”) to the development of a shallow marine clastic-carbonate platform during the Jixian System, reflecting progressive tectonic stabilization and paleoenvironmental transitions.

This study systematically analyzes the spatial distribution characteristics of tourism resources and the equilibrium of cultural tourism development in Macau using a geographic information system (GIS). Through methods such as kernel density analysis, average nearest neighbor index, Thiessen polygons, and standard deviation ellipse, the spatial patterns and influencing factors of Macau’s tourism resources are revealed. The findings indicate that Macau’s tourism resources exhibit significant spatial clustering, primarily concentrated in the historic urban area of the Macau Peninsula, forming a “single-core center + secondary ring-shaped” distribution pattern centered on cultural heritage. Architectural landscapes and World Heritage resources are highly aggregated, while geological, aquatic, and biological landscape resources are dispersed. Spatial equilibrium analysis shows that Taipa and Coloane, due to differences in development timelines, are dominated by modern entertainment facilities and natural ecological resources, respectively, forming a complementary yet non-equilibrium tourism structure with the Peninsula. Natural and social factors jointly shape the distribution characteristics of Macau’s tourism resources. The study recommends future integrated planning for heritage conservation in the Peninsula and tourism development in emerging areas, enhanced regional collaboration, and optimized resource allocation to promote high-quality, balanced tourism development in Macau. This research provides spatial decision-making support for Macau’s goal of becoming a world-class tourism and leisure hub while expanding the application of GIS technology in micro-scale tourism resource analysis.

The urban road surface condition, encompassing elements such as road surface damages and facility distributions, which collectively refers to bump features of road surface (BFRS), is critical for road maintenance and management in smart cities. Traditional BFRS collection methods relying on specialized equipment are often time-consuming and labor-intensive, whereas crowdsourced data frequently suffers from inconsistent quality and reliability. To address these issues, the BFRS is detected and integrated via a mobile sensing framework (MSF), which leverages multi-modal Internet of Things sensors, including global positioning system, gyroscope, accelerometer, camera, and a 4G module. First, the MSF was designed, and a comprehensive set of multi-modal sensors was seamlessly integrated into a Jetson Nano, and further deployed on the vehicle. Second, the software for BFRS computing was implemented to process the collected multi-modal sensor data. This software comprises three key components: the collection of multi-modal sensor data at the mobile end, data processing on the server side, and map interaction and visualization via the web browser. Finally, the fine-tuned TimesFM model (FTTM) dedicated to BFRS acquisition was proposed. BFRS were detected using spatially transformed acceleration data derived from the Inertial Measurement Units. These detections were temporally synchronized and integrated with corresponding road images identified by a specially trained YOLO model. Experiments were conducted, and the BFRS was detected and integrated using the proposed FTTM, with results presented through web visualizations. The results demonstrated that the proposed method effectively captures the road surface condition using the MSF, highlighting its significant potential for applications in smart cities.

To address the challenges associated with the joint inversion of Rayleigh wave dispersion curves and Vertical Electrical Sounding (VES) data, we propose an Improved Sine Cosine Algorithm (ISCA). The original Sine Cosine Algorithm (SCA), which uses sine and cosine functions for optimization, suffers from limitations such as suboptimal control parameter selection and inadequate population diversity during evolution. To overcome these issues, we introduce several enhancements: chaotic mapping for population initialization, an exponential position updating strategy, and a novel offspring generation mechanism. We first assess ISCA’s optimization capabilities by testing it on five complex benchmark functions, where it consistently outperforms the original SCA. We then apply SCA, ISCA, and the classical Particle Swarm Optimization (PSO) algorithm to the joint inversion of Rayleigh wave dispersion and VES data under controlled geological models, both with and without noise. The results demonstrate that ISCA offers improved accuracy and robustness in handling joint inversion problems. When applied to field data from the Besirli site in Türkiye, ISCA outperforms both SCA and PSO, yielding lower fitting errors, better alignment with prior geophysical interpretations, and enhanced solution stability. These results underscore the effectiveness of ISCA for the joint inversion of Rayleigh wave and VES data and highlight its broader potential in geophysical inversion tasks. Furthermore, the integration of these two datasets provides complementary constraints that significantly improve the reliability and resolution of subsurface models.

Wildfires are a main reason of environmental changes in natural ecosystems. While prevention and suppression of forest fires are crucial components of a forest management plan, it is of paramount importance to support decisions with accurate and tangible information. Geospatial data have been a useful source in this aspect, and various techniques have been proposed to define the zonation of forest fire risk in fire-prone areas. In this study, we classify and map forest fire risk in a peat swamp forest in Khreng sub-district, Thailand. Geospatial, climatic, and topographic data (namely elevation, slope, rainfall, land use/land cover, normalized difference vegetation index, and proximity to rivers, settlements, and roads) are fed into the analytic hierarchy process (AHP) and Geographical Information System (GIS) methods to map the area into five fire risk categories. The results are validated based on 70 historic fire events. About 88.57% of the ground truth fire incidents occurred at locations characterized by our assessment as very high risk class and 11.43% in the high risk class. It is suggested that the synergy of geospatial data and GIS and AHP techniques yields useful and accurate fire zone maps, which may aid addressing future fire prevention measures and forest management of fire-prone areas.

Long-term stability of high-speed railway roadbeds is a complex multi-criteria decision-making problem. The stability of cement-improved sandy roadbeds is investigated and evaluated in order to efficiently identify and control risk, assure train safety, and optimize resource allocation. This study relies on bibliometric analysis to filter out roadbed assessment indexes and create an evaluation index system. The Analytic Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution evaluation model is established, and finite element software is used to simulate the actual project, allowing for dynamic stability analysis of the roadbed and lateral comparison of each scheme. The roadbed’s dynamic stability analysis and lateral comparison are completed, as well as the analysis and optimization of the roadbed parameters. The results reveal that a 3:7 pulverized soil ratio and 6% cement mixing improve roadbed stability by 5%. The effect of cement was larger than that of silt. The proposed model’s evaluation results were similar to the field test, demonstrating its dependability, and the research findings can be used to construct a cement-improved aeolian sand high-speed train foundation.

The Khorat UNESCO Global Geopark in northeastern Thailand features a distinctive double cuesta landscape, the “Khorat Cuesta,” developed in Mesozoic sandstones of the Phra Wihan, Sao Khua, and Phu Phan Formations. Although the geology of the Khorat Plateau has been extensively studied, the role of rock properties in controlling geomorphological development remains poorly understood. This study investigates the relationships between lithological, physical, chemical, and mechanical properties of sandstones and their influence on cuesta evolution. Field investigations were conducted at eight localities within the Geopark, complemented by laboratory analyses of petrography, X-ray diffraction, density, porosity, uniaxial compressive strength, and slake durability. Geomorphological analysis incorporated geographic information system and remote sensing using 30 m resolution Shuttle Radar Topography Mission data. Results reveal significant lithological variability among the three formations. Phra Wihan and Phu Phan sandstones, though quartz-rich and forming prominent escarpments, display relatively low strength (R2–R4) and degrade rapidly under slake durability testing. In contrast, Sao Khua sandstones, characterized by fine grain size and calcite cementation, exhibit higher strength (R4–R5) and greater resistance to mechanical breakdown, though they remain vulnerable to chemical dissolution. These differences account for contrasting geomorphic expressions: mechanical weathering dominates in the Phra Wihan and Phu Phan Formations, whereas chemical weathering, particularly acid-induced dissolution, plays a key role in the Sao Khua Formation. Contrary to models emphasizing tectonic uplift or mass wasting in other cuesta systems, the Khorat Cuesta is primarily shaped by differential weathering of sedimentary strata with contrasting properties. These findings underscore the importance of lithological control in cuesta evolution and provide new perspectives for geopark evaluation, geoconservation, and geotourism interpretation. By linking rock properties to geomorphic development, this study contributes to understanding landscape evolution in northeastern Thailand and enhances the scientific value of the Khorat Geopark. Graphical abstract

To explore the mechanisms of spatiotemporal changes in carbon storage (CS) and identify optimal pathways for achieving carbon neutrality. Existing studies have revealed the intrinsic relationship between land use changes and carbon storage. However, research gaps remain regarding the spatiotemporal driving mechanisms of CS in urban agglomerations from an economic-ecological gradient perspective. This study integrates a hybrid machine learning framework (coupling Categorical boosting-Shapley Additive exPlanations and GeoDetector based on optimal parameters-Wavelet analysis) to construct a tripartite CS assessment system encompassing “gradient analysis-scenario simulation-mechanism identification.” Based on the Technique for Order Preference by Similarity to Ideal Solution method, multi-gradient spatial units in the Yangtze River Delta were delineated. The PLUS-InVEST model was employed to simulate the spatiotemporal evolution of CS under business as usual, cultivated-land protection, and ecological protection (ELC) scenarios from 2030 to 2060, thereby revealing the gradient response mechanisms in ecological governance of territorial space. The findings indicate that (1) from the gradient difference dimension, the expansion of built land from 2000 to 2020 led to a continuous decline in CS, with the decline intensity in high-economic-gradient zones exceeding that in high-ecological-gradient zones, primarily due to significant reductions in forest and arable land in high-economic-gradient areas. (2) Spatiotemporal coupling characteristics revealed an increasing coastal-inland gradient and a decreasing north-south gradient. The fluctuation coefficient of CS in high-economic-gradient zones was 50% higher than in ecological zones. (3) Mechanism analysis demonstrated that natural factors (digital elevation model, slope) exerted decisive explanatory power over CS changes within gradient zones, while the nighttime light index exhibited a secondary driving effect in high-economic-gradient zones. Multi-model validation indicated that ecological governance policies could effectively trigger “gradient transitions” in CS by enhancing the interaction effects between net primary productivity and topographic factors. The proposed multi-gradient machine learning analytical framework provides a decision-making pathway of “gradient diagnosis-process simulation-policy response” for coordinated CS management in urban agglomerations and ecological governance of territorial space.

Earthquake earth pressure is one of the key parameters for the seismic design of retaining structures. In this work, the seismic earth pressure calculation methods used in the design specifications of retaining walls, bridge abutments, and other retaining structures in various countries are summarized. Moreover, different calculation methods and examples are analysed and compared. A comparison shows that most of the specifications used or modified the M-O method for calculating seismic active earth pressure, and the calculation approach is basically consistent. The difference in the seismic active earth pressure at the bottom of the retaining walls calculated with various specifications gradually increases with increasing seismic peak acceleration. The coefficient of seismic active earth pressure and the resultant force of seismic active earth pressure both increase with increasing seismic peak acceleration. The calculation method, which considers the additional load at the top, results in a seismic earth pressure resultant force point at a height of above 1/3 H . When calculating the seismic active earth pressure of reinforced soil structures under various specifications, the effect of reinforcement materials on the reduction in wall back earth pressure is not considered, and the calculation results are relatively conservative. The comparison results can provide a reference for the optimization and improvement of the seismic design of retaining structures, such as reinforced earth retaining walls.

This study investigates the petrological and geochemical characteristics of basalts from the Gomer area in the Northwestern Ethiopian Volcanic Province to explore their petrogenesis and mantle source characteristics. Traverse-based field mapping, petrographic investigation, and whole-rock geochemical analysis were conducted. The whole-rock geochemical analysis of basalts for major and trace elements was performed using inductively coupled plasma atomic emission spectrometry and inductively coupled plasma mass spectrometry, respectively. The Gomer area comprises different textured basalts, including vesicular, amygdaloidal, scoriaceous, massive basalt, and interbedded agglomerates. These basalts are primarily composed of minerals such as olivine, clinopyroxene, plagioclase, and opaque phases in varying proportions. They exhibit highly porphyritic to glomeroporphyritic textures with abundant phenocrysts of clinopyroxene and plagioclase and subordinate olivine set in an intergranular textured groundmass of the same mineral assemblage. Based on the proportion of phenocryst mineral assemblage, the studied samples are grouped as plagioclase-pyroxene phyric basalt and plagioclase phyric basalt. Geochemically, the analyzed samples are tholeiitic in composition, with hypersthene- and quartz-normative mineralogy. The Gomer basalts exhibit trace element characteristics and ratios (e.g., Zr/Nb = 12.4–19.8, La/Nb = 1.13–1.5) similar to E-MORB. Low (La/Yb) N ratios (2.19–3.85) and relatively flat HREE patterns [(Tb/Yb) N = 1.29–1.7 and (Dy/Yb) N = 1.27–1.57] indicate a high degree of partial melting of an E-MORB-type mantle in the spinel stability field. Overall, the petrogenesis of the Gomer basalts is primarily governed by fractional crystallization involving olivine, clinopyroxene, plagioclase, and occasional spinel, with minimal crustal interaction. The geochemical characteristics and trace element ratios support derivation from an E-MORB-type mantle source. These findings provide new insights into mantle heterogeneity in continental rifting settings and allow comparisons with other large igneous provinces, such as the Deccan Traps and Icelandic basalts.

To address the insufficient ground deformation monitoring coverage in non-urban areas of the Xiong’an New Area in existing studies, this study employs Distributed Scatterer Interferometric Synthetic Aperture Radar technology to systematically analyze the spatiotemporal characteristics and evolution of ground deformation using 51 ascending Sentinel-1A images acquired between March 2021 and March 2023. The study not only accurately obtained maps of annual average subsidence rates and cumulative deformation amounts, but also delved into the potential driving mechanisms of subsidence. The results reveal significant spatiotemporal heterogeneity in land surface deformation within Xiong’an New Area, with pronounced accelerating subsidence observed in the northeastern and southwestern regions. The maximum deformation rate reaches –22.717 mm/year, and the maximum cumulative subsidence is –58 mm. Further analysis indicates that geomorphological features, shallow groundwater activity, and geothermal resource exploitation are the primary factors controlling the observed subsidence. These findings provide crucial data support for land subsidence monitoring, risk assessment, and management in Xiong’an New Area, offering valuable insights for ensuring the sustainable construction of the new district.

Lake-phase organic-rich shales are widely developed in the fifth member (Xu5 Member) of the Triassic Xujiahe Formation in the Sichuan Basin. However, due to the lack of understanding of the paleoclimate and paleoenvironment, the mechanism of organic matter enrichment in the Xu5 Member shale is controversial. To address these issues, this study takes the X5 Member in the Tianfu area as a case study. By analyzing its paleoenvironmental characteristics and correlating with organic matter development, we elucidate the influence of paleoenvironmental conditions on organic matter enrichment patterns in X5 shale. The results show that: (1) the shale of the Xu5 Member has high total organic carbon content and the organic matter type is dominated by type III, which has good potential for anger. (2) Chemical index of alteration (CIA)–weathering index of Parker (WIP) cross-plots indicate first-cycle deposition of the Xu5 Member shales with significant terrigenous input. La/Th, Hf, and Al 2 O 3 /TiO 2 ratios collectively suggest relatively slow sedimentation rates. Provenance analysis reveals dominant felsic igneous sources with minor mafic contributions. C -values and Sr/Cu ratios consistently indicate semi-humid to humid paleoclimatic conditions during deposition. Sr/Ba (<0.5) and Al 2 O 3 /MgO (>7) ratios constrain paleosalinity to freshwater-brackish conditions. Redox proxies (Ni/Co, U/Th, V/Cr, U EF , Mo EF ) uniformly demonstrate oxygenated bottom-water conditions. Productivity indicators (P/Ti, Cu/Ti, Ba bio ) reveal elevated paleoproductivity in early Xu5 3 to Xu5 2 , with lower values in overlying intervals. (3) The organic matter enrichment of the shale in the Xu5 Member is mainly affected by the redox conditions of the bottom water, paleoproductivity, and paleosalinity, and the more reducing the bottom water is, the higher the paleoproductivity and the higher the paleosalinity are, the more favorable the organic matter enrichment is. In addition, in the sub-member of Xu5 1 and Xu5 2 , paleoclimate, land-source input, and sedimentation rate are also secondary factors affecting the degree of organic matter enrichment.

Transient river profiles with slope-break knickpoints record past and present tectonic information. The channel upstream of a knickpoint represents a relict profile in equilibrium with previous tectonic forcing, while the downstream segment reflects current uplift rates. Relict profile reconstruction projects the paleo-channel across the knickpoint to estimate the minimum incision depth. Traditional methods, such as slope-area analysis and the integral approach, can introduce errors due to data smoothing, resampling, and residual autocorrelation in chi-elevation regressions. To address these limitations, we propose an improved integral approach equipped with generalized least squares to eliminate autocorrelated residuals and provide accurate estimates of channel concavity and steepness indices. These indices are then used to reconstruct the relict profile and estimate incision depth. We validate our method using a synthetic transient river profile generated by 1D numerical modeling and further apply it to rivers crossing the mountain ranges bounding the Taiyuan Basin in northern China. Our results identify slope-break knickpoints, reconstruct relict profiles, and estimate incision depths, which provide lower limits of fault throw since the late Miocene to early Pliocene. This improved method offers a robust approach to analyzing transient river profiles and quantifying tectonic deformation.

Highway landslides in hilly regions pose significant risks to transportation infrastructure. Current monitoring systems, confined to jurisdictional boundaries, neglect cross-administrative slopes, creating systemic gaps in hazard management. This oversight is exacerbated in densely vegetated areas lacking standardized detection protocols, where external landslides may trigger destructive debris flows impacting highways. A two-stage framework is proposed in this work. Failure mechanism of failure landslides, located beyond the jurisdictional redline of a highway, are first analyzed based on small baseline subset synthetic aperture radar interferometry (SBAS-InSAR). The obtained deformation thresholds together with SBAS-InSAR are then used to quantify pre-failure deformation and identify landslide risk along the same highway. Results show that vegetation destruction and rainfall are primary triggers for the studied landslide and subsequent debris flows. SBAS-InSAR revealed an annual deformation threshold of 21.4–32.8 mm/year. High-risk slopes within 500 m of the highway with sparse vegetation require prioritized monitoring during rainy seasons. This approach offering actionable criteria for authorities to monitor extra-jurisdictional slopes. By integrating failure mechanism analysis with deformation thresholds, the study bridges jurisdictional gaps in infrastructure risk management, enhancing resilience against previously overlooked cross-boundary hazards.

At present, many studies quantify the effects of climate change and human activities on annual runoff, but ignore the effects of human activities on the intra-annual runoff. Based on monthly scale meteorological and hydrological data from 1982 to 2019, a multi-temporal Budyko framework was constructed to quantitatively assess the influence of climate and human factors on intra-annual runoff change in Ganjiang River Basin. The results indicated that: (1) Annual, summer, autumn, and winter runoff depth all showed an insignificant upward trend, and spring runoff depth was an insignificant downward trend. (2) Climate factors had a positive impact on annual, spring, summer, autumn, and winter runoff depth growth, with a contribution of 182.76, 12.35, 141.04, 16.74, and 14.01 mm, respectively. (3) Human factors had a negative impact on the annual, spring, summer, and autumn runoff depth growth, with a contribution of −56.11, −21.55, −36.92, and −8.46 mm, respectively. Human factors had a positive impact on winter runoff depth growth, with a contribution of 9.44 mm.

Agriculture in Najran City plays a crucial role in supporting the local economy. Nevertheless, the region faces sustainability, resource management, and environmental degradation challenges. Descriptive and somewhat manual techniques for assessing land-use scenarios and classifying agricultural land changes can be tedious and prone to human error, and they remain inefficient when analyzing rapidly changing terrains. AI techniques offer promising solutions to overcoming such shortcomings, enabling automated, accurate, and scalable analyses of highly intricate satellite imagery datasets. This study proposes a hybrid artificial intelligence-based topographic analysis model to improve agricultural development in Najran. Spatial features extraction from the images of those two models through spatial information fusion would, in the post-fusion, mean an increase in redundancy and irrelevant features and an increase in computational complexity. Therefore, the next step was using a PCA algorithm to remove unessential features before feeding essential features into the artificial neural network (ANN) algorithm. The resulting important features are then classified using an ANN. The system was then implemented using Landsat 8 satellite imagery for 2013–2023. The results indicated that the developed hybrid EfficientNetB7–ShuffleNet with ANN system achieved an accuracy of 97.11% in 2013 and 97.01% in 2023. It also achieved a Recall rate of 97.66 and 97.54% during the period 2023 and 2023, respectively, and an F 1-score of 96.77 and 97.53% during the period 2023 and 2023.

Sustainable agriculture depends heavily on precise LULC classification to support soil conservation, water resource planning, and environmentally conscious land use. This study proposes a hybrid deep learning system integrating VGG16 and EfficientNetB7 models with a Random Forest (RF) classifier to classify agricultural and other LULC types in Najran, Saudi Arabia, utilizing digital elevation models (DEMs) and Indian Remote Sensing Advanced Wide Field Sensor satellite data from 2020. A stereo-derived DEM was used to extract topographical features, which, combined with multi-temporal imagery, were processed through VGG16 and EfficientNetB7 for spatial feature extraction. The Grasshopper Optimization Algorithm was applied to select the most essential features and remove the unimportant and redundant ones. The features were then fed into an RF classifier to classify the Najran terrain map efficiently. Evaluation of the hybrid system showed promising results for classifying the Najran terrain map, achieving an accuracy of 94.2%, precision of 79.88%, recall of 79.22%, F 1-score of 79.53%, and specificity of 96.01%. The system demonstrated robust performance in differentiating agricultural lands from urban and natural terrains, enabling efficient monitoring of land use patterns. This approach supports sustainable agricultural practices and environmental stewardship by providing decision-makers with high-resolution, automatically classified land maps for strategic planning in arid regions, such as Najran.

This study examines the spatiotemporal variability of shallow groundwater in the Huaibei Region, Anhui Province, with a focus on factors such as precipitation, human extraction, and historical overexploitation. Multiple time-series analyses, including the Sen and Mann–Kendall trend tests and Morlet wavelet analysis, are employed to assess groundwater trends and their relationship with precipitation, aiming to support sustainable water resource management. Findings reveal a general trend of “initial increase followed by decrease” in groundwater depth across the region, with varying rates of decline from north to south. A dominant 35-year cycle in groundwater depth is identified, with an oscillation center in the 1990s to early 2000s. Wavelet coherence analysis reveals a significant periodic resonance between groundwater depth and precipitation, with lag effects exhibiting spatial variations. The study offers insights into the temporal characteristics of groundwater trends and the increasing lag effects of precipitation on groundwater levels from north to south, geographically. This study systematically reveals the temporal scale characteristics of groundwater evolution and the spatial variability of the lag effect of precipitation, providing important references for the scientific management of shallow groundwater in overexploited areas and supporting the development of regional water resource management strategies. The findings offer direct decision-making support for water resource authorities, planning agencies, agricultural sectors, and environmental protection departments in Anhui Province and the Huaibei Region, while also holding practical value for broader application in other groundwater overexploitation zones across East China and the entire country. Graphic abstract The interaction mechanism model between shallow groundwater and precipitation.

Multispectral and hyperspectral remote sensing represent critical instrumental methodologies that significantly enhance geological mapping and facilitate the identification of mineral resources. Collisional remnants of Himalaya-Hindukush northern Pakistan offer a unique opportunity to explore the hydrothermal deposits hosting economic minerals, inaccessible due to rugged and steep topography. The primary applications of ASTER and ZY1-02D remote sensing data include the extraction of alteration zones to detect economically significant minerals and depicting lithological units. To assess potential mineralization zones in the rugged terrain of northern Pakistan, herein, we implement integrated remote sensing techniques like false color composite, decorrelation stretching, band ratio, machine-learning techniques (maximum likelihood classification, spectral angle mapper, neural net classification, and match filtering), and validation (field observations). The results revealed 11 lithological units and several mineralization zones with key indicator minerals. These zones contained clay minerals, i.e. kaolinite, muscovite/sericite, and propylitic minerals, including calcite and chlorite, indicating copper and iron mineralization. The detected minerals emphasize that hydrothermal alteration in the region, controlled by the hot fluids from the granitic plutons (Tarichmir, Garam Chashma, and Kafiristan) of northwest Pakistan. The acquired mineral signature from remote sensing data was validated with ground truth observations, spectrometry, and petrographic analysis. Furthermore, this study proved the effectiveness of implemented approaches in delineating mineralization zones and discriminating lithological units.

With the rapid development of the social economy, nitrogen dioxide (NO 2 ), a trace gas that is an air pollutant, has dual impacts on human health and climate change. In this study, the Google Earth Engine platform is used to retrieve the vertical column concentration inverted by the Tropospheric Monitoring Instrument (TROPOMI) sensor onboard the Sentinel-5 Precursor satellite, and the characteristics of the spatial–temporal distribution of the tropospheric NO 2 column concentration in Shandong Province from 2019 to 2023 are analyzed. The results show that there is a high correlation between the NO 2 column concentration retrieved from the TROPOMI and the NO 2 concentration observed at ground-level monitoring points in Shandong Province ( R = 0.8289). The distribution of the NO 2 column concentration in Shandong Province exhibits a progressive structure of “high in the west and low in the east” with significant spatial heterogeneity. In addition, the fluctuation in the NO 2 column concentration in Shandong Province has obvious cyclical and seasonal characteristics, and the change rate is “summerflat in spring and autumn, high in winter and low in summer.” The results of the geographical detectors indicate that temperature has the greatest influence on the NO 2 column concentration in Shandong Province ( q = 0.5). The geographically weighted regression model indicated that the NO 2 column concentration was negatively correlated with the topography, temperature, and precipitation, and the correlation coefficients are −0.010, −0.952, and −0.330, respectively. It is positively correlated with the intensity of human activities, the degree of urbanization of the land, and the population density, and the correlation coefficients are 0.1610, 0.982, and 0.001, respectively.

Geothermal energy piles (GEPs) combine structural load-bearing and heat-exchange functions, offering a sustainable solution for energy-efficient building foundations. This study presents a fully coupled thermo-hydro-mechanical finite element model in Plaxis 2D, calibrated against field data from the Swiss Tech Convention Center. The model successfully reproduced pile head displacements (upward heave during heating, Residual settlement after cooling) and vertical strain distributions observed in experiments, confirming its validity. A four-stage heating-cooling cycle (with a temperature difference of 21 K) revealed that volumetric expansion and contraction are most pronounced in the upper alluvium and redistributed into stiffer moraine layers. Sensitivity analysis identified the concrete modulus as the dominant factor, with notable influence of soil elastic moduli and Poisson’s ratio in specific layers. Parameter variation analysis showed that ±20% changes in these inputs had a negligible effect on the response, emphasizing the governing role of pile stiffness and thermal loading amplitude. These findings provide a robust framework for assessing GEP performance and highlight the importance of elastic properties and construction quality over minor soil variability in typical design scenarios.

The upstream valley-type tailings dam exhibits a longitudinal profile that resembles an inverted triangle or a shape approximating an inverted triangle. This unique dam structure and its mechanical characteristics mean that the influence of longitudinal stress on the stability of the dam cannot be overlooked. However, the two-dimensional Swedish slip circle method calculation model does not take into account longitudinal stress, necessitating the development of a three-dimensional model specifically for the stability analysis of upstream valley-type tailings dams. Utilizing the Beijing Lizheng Geotechnical Calculation Software 5.3 standalone version, a three-dimensional calculation model was constructed based on the Swedish slip circle method. Taking the Cangyuan Jinla Lead-Zinc Mine tailings dam as an engineering example, this model was used to calculate five cross-sections, including 1–1′, of the dam. The results were then compared and analyzed with those obtained from the Swedish slip circle method, the simplified Bishop method, and the Janbu method. Using this three-dimensional model, the safety factors for cross-sections 1–1′, 2–2′, 3–3′, 4–4′, and 5–5′ of the tailings dam were calculated to be 2.798, 1.348, 1.332, 1.466, and 2.134, respectively. Analysis concluded that the most dangerous sliding surface of the tailings dam is located at the 3–3′ cross-section, with an overall dam safety factor of 1.332, indicating that the tailings dam is in a relatively stable condition. The safety factor of the tailings dam calculated using the finite element software MIDAS is 1.3531, verifying the accuracy of this three-dimensional model. For upstream valley-type tailings dams, the results from ordinary two-dimensional models tend to be on the dangerous side and thus should be corrected using this three-dimensional model to obtain more accurate and conforming safety factors.

Actual evaporation is a critical component of the water cycle and plays an important role in natural environmental change. Research on actual evaporation are significant for ecological conservation and efficient water resource utilization. Therefore, the aim of this study is to analyze the variation characteristics of monthly actual evaporation in the Jialing River (JLR) of China and quantitatively calculate the contribution of different factors to monthly actual evaporation. In this study, based on the monthly actual evaporation data of Beibei hydrological station in the JLR from 1982 to 2019. We first used cumulative anomaly and Mann-Kendall (M-K) mutation analysis methods to determine mutation year. The trend-free prewhitening M-K trend test was then used to analyze the trend of actual evaporation on a monthly scale. Finally, we applied monthly-scale Budyko model to quantitatively analyze the contribution of climate change and human activities on actual evaporation on monthly time scales. The results showed that (1) The mutation year for actual evaporation in the JLR was 1994. (2) The actual evaporation displayed a significant downward trend in January and October to December. Actual evaporation displayed a significant upward trend in March to June. (3) The distribution of monthly actual evaporation in different periods all presented an obvious “single peak” distribution. Actual evaporation reached its maximum in July. (4) The main factor leading to the actual evaporation change was climate change.

Landslide susceptibility assessment in arid mountainous regions requires specialized modeling approaches. This study, combining the information value (IV) modeling and machine learning, develops a coupled model approach for Minfeng County, Xinjiang, that a complex arid zone with frequent landslides. From the ten influencing factors, seven key factors were identified through factor covariance and correlation studies, so as to construct the landslide susceptibility evaluation index system. On this basis, using 135 landslide samples and combining the output of the information value (IV) model with four machine learning algorithms–support vector machine (SVM), logistic regression (LR), random forest (RF), and artificial neural network (ANN)–we constructed four coupled models (IV-LR, IV-ANN, IV-SVM, and IV-RF) for landslide susceptibility evaluation. Critical results are as follows: (1) proximity to rivers/roads and vegetation density (NDVI) dominate landslide triggers and (2) all models showed high accuracy (area under curve [AUC] > 85%) with 7:3 training:testing validation and the IV-RF model achieved optimal high-susceptibility zone delineation (accuracy = 82.71%; AUC = 0.8945). This method provides a technical reference for landslide disaster prediction, prevention, and mitigation in arid mountainous areas of Xinjiang.

The annual spatiotemporal variation for the comprehensive accessibility of service facilities (CASF), expressing the dynamic development of urban service, has not been routinely monitored adequately (such as multi-type facility interactions, policy-driven dynamics, and persistent inequities). To address this gap, we propose three key innovations: (1) Utility-improved accessibility model: A novel framework integrating 15 facility types through entropy-weighted utility indices, overcoming the single-type limitation of traditional models. This model dynamically couples facility diversity (point of interest density), population demand (mobile big data), and time-decayed travel impedance (route planning application programming interface), enabling multi-dimensional CASF evaluation. (2) Integrated spatiotemporal methodology: We synergistically combine four methods, standard deviational ellipse (directional trends), space-time cube (3D continuity), emerging hotspot analysis (cluster evolution), and time series clustering (trajectory phases) to capture multi-year interactions between policy-driven urban expansion and accessibility dynamics. (3) Policy-ready findings: Our analysis of Nanjing (2016–2022) reveals that CASF exhibits multi-center agglomeration (centroid shift toward Jiangbei New District) with divergent trajectories persistent hotspots in historic cores (Gulou District) vs stagnant peripheries (northern Qixia). These patterns directly align with Nanjing’s 2021–2035 master plan. The framework provides actionable insights for policymakers to prioritize investments in intensifying hotspots (e.g., Jiangbei’s commercial hubs), address systemic deprivation in persistent cold spots (e.g., Qixia’s underserved areas), and monitor phased impacts of transportation projects through CASF trajectory clusters.

A new pre-treatment method is described for the disaggregation of fine-grained sedimentary rocks (FSRs). The main steps include (1) heat the samples in a muffle furnace and then put them in a beaker containing sodium sulphate solution of a certain concentration; (2) freeze the beaker containing the samples in a refrigerator; (3) take out the beaker and put it into a water-bath pan to maintain a constant temperature. Using this method, FSRs were disaggregated in Well H-1 (3756.42–3769 m) of the Dongying Depression and the particle size data were obtained to analyse sealing capacity. The result shows a close correlation between particle size distributions and capillary entry pressure, indicating that particle size distribution is one of the most important factors influencing sealing capacity. This method shows favourable performance in most of the FSRs except for rocks with greater degrees of metamorphism. The advantages of this method include the decreased use of chemical reagents with strong corrosion potential and obviation of the need to use a grinding pestle, which reduces the impact on primary particles.

Hard-shell layers often form during the migration and segregation of fine-grained sediments and play a critical role in influencing the mechanical behavior of seabed soils, with implications for marine geological hazards. Understanding the mechanisms that govern the formation and evolution of these layers is therefore essential for coastal and estuarine geotechnical engineering. In this study, laboratory experiments were conducted to investigate the formation mechanisms of hard-shell layers in saturated silty soils subjected to two types of controlled physical disturbances: vertical hammering and horizontal rotating wheel-induced shear. Using high-speed particle image velocimetry, pore pressure sensors, and earth pressure measurements, the study analyzed the coupled evolution of the force field, particle migration field, and liquefaction behavior under different disturbance modes. Results show that liquefaction is a prerequisite for hard-shell layer formation, allowing fine particles to migrate upward while coarse particles settle, leading to effective particle separation. Hammering produced high pore pressure and strong vertical particle mobility, resulting in a thin but dense hard-shell layer with high strength. In contrast, the rotating wheel disturbance generated moderate liquefaction and vortex-driven particle segregation, forming a thicker hard-shell layer with relatively lower mechanical strength. The findings highlight a trade-off between shell thickness and strength depending on the disturbance type and suggest that the mechanism of fine–coarse particle separation under liquefaction conditions is key to shell formation. This research provides a theoretical and experimental basis for understanding internal liquefaction processes in silty coastal slopes, particularly under the influence of waves or seismic activity following landslides.

We applied a multidimensional approach to analyze spatiotemporal patterns and driving mechanisms of agricultural carbon emission efficiency (CEE) in Anhui Province, providing evidence for region-specific carbon reduction policies. Using city-level panel data from 2010 to 2022, we analyzed spatial agglomeration patterns of agricultural CEE through global and local Moran’s I indices and Local Indicators of Spatial Association scatter plots. Agricultural CEE in Anhui Province showed consistent annual growth throughout the study period. Spatial heterogeneity analysis revealed a distinct geographical pattern – high in the south and low in the north, high in the west and low in the east – characterized by positive spatial autocorrelation. A random forest model weighted by SHapley Additive exPlanations values identified key driving factors, with agricultural plastic film use intensity emerging as the primary inhibiting factor of agricultural CEE. Spatial Durbin model results showed that agricultural inputs – fertilizers, pesticides, plastic films, and irrigation – generate significant “spillover effects,” impacting both local and neighboring regions with nonlinear spatial characteristics. Coupling coordination degree analysis indicated improving alignment between agricultural output value and CEE, though significant optimization potential remains. We established an efficiency assessment-mechanism identification-policy response framework, offering a geographically weighted empirical approach for advancing agricultural low-carbon transformation. Graphic abstract

Sandy dolomite in the Yunnan Dianzhong Water Diversion Project tunnels exhibits low strength and high permeability, frequently leading to surrounding rock damage and water surge disasters during excavation. Understanding the failure mechanisms under excavation disturbances is critical for engineering safety. The damage mechanisms of the sandy dolomite surrounding rock, influenced by particle gradation, sanding degree, fault location, and burial depth, remain poorly quantified. A particle flow code numerical model was established to simulate tunnel excavation disturbances. The model incorporated dolomite particle size distributions (0.1–0.6 × 10 −2  m), fault proximity (0.06–0.12 m), and burial depths (0 to −0.2 m), with stress fields calibrated to in situ conditions (lateral pressure coefficient k = 1; stress = 2.056 × 10 7  N/m 2 ). (1) Total displacement and the contact force increased with larger dolomite particle size (e.g., the vertical displacement increased from 2.6 × 10 −7  m to 5.0 × 10 −7  m with increasing burial depth); (2) the higher sanding degree significantly reduced the elastic modulus (by 18–32%) and increased Poisson’s ratio; (3) displacements were reduced by 35–50% when the fault–tunnel distance exceeded 0.12 m; (4) increasing burial depth significantly amplified all displacements (e.g., vertical displacement triples horizontal displacement). The stress–displacement response of the sandy dolomite surrounding rock was governed by particle gradation, sanding-induced mechanical degradation, fault–tunnel interaction, and depth-dependent geostress. Mitigation strategies should prioritize particle size control and fault avoidance.

Although northwest China faces challenges in achieving carbon neutrality, it has the potential for ecological carbon sequestration. Based on panel data from five northwestern Chinese provinces from 2003 to 2023, this study used an SBM-data envelopment analysis model to evaluate agricultural ecological efficiency (AEE) and analysed the spatial evolution and driving factors using ArcGIS and the Moran index. The results show that: (1) AEE shows a fluctuating upward trend with significant differences between provinces, with Shaanxi performing best and Ningxia performing worst; (2) the evolution of AEE can be divided into three stages: The early stage (2003–2011) was dominated by input factors; the middle stage (2012–2017) was influenced by ecosystem services; and the late stage (2018–2023) was the key constraint. Rural electricity consumption, agricultural diesel consumption, and the effective irrigation area were the core driving factors. The expected output affected the spatial pattern through a synergistic effect. Suggestions: (1) Establish an inter-provincial cooperation mechanism to narrow the regional gap through ecological compensation and technology transfer. Promote water-saving irrigation and organic agriculture in low-efficiency areas (e.g. Ningxia and Qinghai) and build green technology demonstration bases in high-efficiency areas (e.g. Shaanxi and Gansu). (2) Promote the green transformation of agricultural energy. Promote solar irrigation and electric agricultural machinery through subsidy policies. Develop intelligent irrigation technology using the Internet of Things. Reduce diesel dependence and improve the efficiency of water resources. This research provides a scientific basis for optimising agriculture and achieving sustainable development in north-west China under the goal of carbon neutrality.

Porous carbonate reservoirs of the Middle East commonly combine high porosity with low permeability, resulting in pronounced heterogeneity in hydrocarbon distribution. This study investigates the mechanisms behind the cyclicality of oil-bearing intervals in the Upper Cretaceous Khasib Formation at the E Oilfield, Iraq. A multiproxy dataset – including detailed core descriptions, thin-section petrography, conventional well logs, and laboratory measurements of porosity, permeability, pore-throat radius, extractable organic matter, and oil saturation – was used to define seven fourth-order depositional sequences (SSQ1–SSQ7), whose lithofacies architectures closely follow high-frequency sea-level oscillations. An effective porosity threshold of 15% was established for hydrocarbon charging. Within these reservoirs, permeability and wettability emerge as the primary controls on oil saturation: permeability dominates below 3 mD, while wettability exerts greater influence at higher permeabilities. Variations in lithofacies and episodic exposure driven by sea-level fluctuations generate heterogeneous reservoir properties and drive the cyclic distribution of oil intervals. Transgressive system tracts predominantly produce poor- or non-oil facies, early regressive tracts host medium- to good-quality oil layers, and oil content declines in late regressive tracts. These results offer critical insights for assessing hydrocarbon potential and predicting the spatial distribution of oil intervals in analogous porous carbonate systems.

The microstructure modeling of porous media is an important foundation for quantitative characterization of media and prediction of its physical properties. A method of applying mathematical morphology to microstructure modeling is proposed to reconstruct digital core models that evolve progressively on pore structure. First, the basic principles of process-based and mathematical morphology methods are reviewed briefly. Then the concept of morphology-based directional and quantitative modification is proposed in detail for the simulation of complex cementation. Finally, the feasibility and validity of this modeling technique are verified by performing the pore structure modification for porous models. The purpose of this modeling method is to directionally and quantitatively reconstruct digital core models with specific pore structures. The strengths lie in diverse inputs, flexible procedures, and controlled results. This work extends the numerical reconstruction of digital core in terms of the microscopic pore structure.

The Dongzhi Plateau, the largest remaining loess tableland on the Chinese Loess Plateau, is a critical region within the Yellow River Basin, and its ecological health is vital for regional ecological security and sustainable development. However, it faces severe habitat degradation under climate change and human activities. Utilizing the InVEST model and the geographical detector method, combined with multi-temporal land use data from 1995 to 2023, this study provides a systematic analysis of the spatial–temporal differentiation of land use and habitat quality, and explores the driving mechanisms behind habitat fragmentation in the Dongzhi Plateau, a representative loess tableland region on the Chinese Loess Plateau. The results showed that: (1) land use change was dominated by the bidirectional conversion of cropland and grassland, alongside a significant expansion of impervious. (2) Habitat quality demonstrated pronounced spatial heterogeneity, characterized by a distinct “core-edge” degradation pattern radiating from urban centers. Notably, while the majority of the area (87.41%) remained stable, an improvement was observed in 7.68% of the region, whereas 4.91% exhibited discernible degradation. (3) Land use change was the dominant driver of habitat quality dynamics, with its explanatory power ( q value) strengthening over time, increasing from 0.8334 in 1995 to 0.8887 in 2023. Its interaction with topographic and socioeconomic factors further enhanced this influence. Agricultural and urban expansion has encroached on ecological spaces, exacerbating habitat fragmentation. We propose spatially differentiated ecological restoration strategies, prioritizing gully ridge areas for vegetation recovery and promoting agricultural restructuring. These findings provide a scientific basis for ecological conservation on the Loess Plateau and support the high-quality development of the Yellow River Basin.

Petroleum exploration requires careful planning due to high risks, expenses, and time consumption. Predicting oil sites is the preliminary step in petroleum exploration. Oil site prediction can be optimized by machine learning (ML) algorithms and techniques. This article highlights the issue of optimizing oil site prediction, which has not been focused on in existing research studies. The article proposes an approach that aims to improve the prediction of potential oil sites. The proposed approach is based on a conceptual framework and an assessment of geological criteria. The criteria are assessed and validated based on a sensitivity analysis. The framework is based on convolutional neural networks (CNNs), a subsidiary of ML, and image processing techniques. It consists of four main steps, which are data preparation, semantic segmentation, model training, and validation. The framework is implemented in a case study in the Fort MacKay region of Alberta, Canada. The implementation of the proposed approach shows promising accuracy for predicting potential oil sites, with a 95% intersection between the predicted potential zone and the actual petroleum site. The main contribution of the proposed approach is a sensitivity assessment of geologic criteria and a CNN-based model to enhance the prediction of potential petroleum sites.

In order to study the change of landscape pattern and its impact on waterbird diversity in Xianghai Nature Reserve, this article analyzes the changing landscape pattern of the reserve from 2014 to 2017 at the landscape and type level. Pearson correlation coefficient and grey correlation analysis were used to determine the relationship between landscape pattern change and waterbird diversity. The results showed that the landscape pattern changed significantly, and marsh wetland (39.03%) replaced cultivated land (30.09%) as the dominant landscape type. At the landscape level, waterbird diversity was negatively correlated with landscape fragmentation and shape complexity (coefficients were −0.993, −0.993, −0.968, and −0.986, respectively; p value <0.05) and positively correlated with landscape connectivity and diversity (coefficients 0.727 and 0.882, respectively; p value <0.05). At the type level, the diversity of waterbirds is mainly affected by landscape fragmentation and patch shape complexity. As marshes and watersheds are major landscapes, waterbird diversity can be enhanced by increasing their area, fragmentation, and shape complexity appropriately.

Efficiently utilizing and excavating existing construction technologies to obtain potential data information represents a significant challenge in the realm of intelligent tunnel construction. Considering the judgment bias inherent to the manual experience of the traditional surrounding rock grading method, this article proposes an intelligent real-time method for surrounding rock grading based on information collection technology and artificial intelligence. This approach relies on studying the connection between multifunctional drilling rig parameters and the properties of the adjacent rock in tunnel building. The method classified the rock surrounding the “Fengjian Expressway in the Three Gorges Reservoir Area” from the Ministry of Communications Science and Technology demonstration project, and the results showed that: (1) it is a multi-method verification protocol synergizing on-site measurements, laboratory tests, and AI algorithms to eliminate subjective judgment errors and it is an optimized parameter selection mechanism identifying critical drilling indices for rock-property correlations and (2) back-propagation neural network model demonstrating 98.6% accuracy – the highest among tested intelligent algorithms. This methodology bridges empirical engineering practices with data-driven intelligence, offering a replicable solution for infrastructure projects in complex geological environments.

Research on the influence of tectonic activity on the morphology of delta distributary channels still lacks systematic methodologies, particularly in sparsely drilled areas. Therefore, this study focuses on the Pu1 oil layer group, utilizing dense well pattern data and 3D seismic data to characterize sedimentary microfacies. The research further examines three key aspects: Analyzing variations in distributary channel microfacies and the relationship between single sandbody thickness and channel width in conjunction with regional tectonic evolution; Investigating the distribution patterns of channel bifurcation points through reconstructed paleotopography; Assessing the control of growth faults on channel orientation by combining identified contemporaneous faults. Key conclusions include: (1) Stable tectonic uplift governs the transition from braided distributary channels to meandering distributary channels, causing a decreasing trend in channel width-to-thickness ratios; (2) When distributary channels traverse peak-shaped (“∧”) paleotopographic features, bifurcation preferentially occurs at elevated positions; (3) Under sufficient sediment supply, growth faults control channel deflection, aligning channel directions with fault strikes and concentrating channels along downthrown blocks of faults. The study demonstrates that tectonic development exerts significant control on delta distributary channel morphology. Characterization of distributary channels in sparsely drilled regions should incorporate paleotectonic influences, warranting greater investment in paleostructural research.

Season is a typical growth cycle unit for vegetation, revealing the spatial distribution and spatial-temporal dynamic changes of fractional vegetation cover (FVC) in parks. This information can provide a scientific basis for optimizing the allocation of park vegetation. This work focuses on People’s Park and Harmony Park in Xinxiang, China, using quarters as the temporal framework and multi-spectral high-resolution unmanned aerial vehicle images as the data source to systematically assess the spatial distribution and spatial-temporal dynamics of FVC. The findings indicated that (1) the annual FVC was primarily characterized by bare and middle areas. Spatially, the FVC in both parks exhibited a pattern of higher values along the periphery and lower values toward the center. In summer, it featured bare and highest areas, while in winter, the park was mainly characterized by bare and lowest areas. The ratios of areas at major FVC grades were significantly larger than those at other grades. In spring and autumn, the ratio of bare areas was notably high, while the distribution of areas at other FVC grades was relatively balanced; (2) the trends of quarterly FVC mean at regional scale resembled a “∩”-shaped pattern, peaking in summer, followed by spring, autumn, and winter. The bare and highest areas fluctuated the most, with their rates of change showing inverse trends. Areas at other FVC grades remained comparatively stable; (3) in the dynamic changes of FVC at pixel scale, the areas with the greatest proportions of improvement and degradation are during the summer-autumn and winter-spring periods. During summer-autumn, basically unchanged and moderately degraded areas prevailed, whereas in winter-spring, basically unchanged and significantly improved areas were more common. The FVC changes at pixel scale vary by season. Overall, the improvement and degradation areas are mainly located on the periphery of the park, internal roads, and areas with less foot traffic, while the basically unchanged areas are concentrated in the hard and water parts; (4) the spatial evolution of FVC within the parks showed positive trends from spring to summer and winter to spring, while primarily negative trends were observed from summer to autumn and autumn to winter. Multiple evolutionary paths were present in each phase, with cross-level transitions being frequent. In the construction of urban parks, efforts should be made to consciously increase the proportion of vegetation in the park’s land types, while meeting functional requirements such as use and viewing. Simultaneously, vegetation planning should be strengthened, including spatial distribution planning, vegetation structure design, and plant selection.

Iron (Fe) is an essential trace element for the growth of phytoplankton in the ocean. Humic substances (HSs) are key components of dissolved Fe-binding organic ligands (Lt). Both Lt and HSs are widely accepted to regulate the distribution of dissolved iron (DFe) and influence its availability to marine phytoplankton and other organisms. This paper provides a concise overview of the historical progression of DFe determination and its speciation, including Lt and HSs, using electrochemical methods. It also reviews applications of these methods in examining the effects of HSs on DFe, drawing from spectroscopy, chromatography, and mass spectrometry data. Electrochemical techniques can measure the concentrations of HSs and the binding capacity of DFe, offering valuable insights into the role of HSs on DFe in marine settings. Spectroscopy, chromatography, and mass spectrometry allow for detailed characterization of the structure, properties, and types of organic ligands and HSs. These methodologies have enhanced our understanding of Lt and HSs, whether of marine or terrestrial origin, as significant ligands for DFe, influencing its concentration, distribution, and circulation. Future research should delve deeper into the mechanisms and chemical properties of Fe complexation with organic matter. Additionally, the impact of various factors on HSs complexes in relation to DFe warrants further exploration, benefiting from synchronous analysis using multiple detection methods. Such advancements would offer crucial insights into the biogeochemical cycling of Fe and enhance various research domains.

Physics-informed neural networks (PINNs) have emerged as a powerful tool in the intersection of machine learning and physical sciences, offering novel approaches to solve complex differential equations inherent in geoscientific phenomena. Despite their growing application, a review of their applications and potential within geosciences remains missing. This review systematically examines the utilization of PINNs in various geosciences such as hydrology, seismology, atmospheric sciences, geophysics, and others, highlighting their ability to integrate physical laws into neural network training processes. It describes the potential of PINNs to improve predictive modeling accuracy, reduce computational costs, and overcome the limitations of traditional numerical methods. The importance of this research lies in its assessment of PINNs’ contributions to geosciences, offering valuable insights for researchers and practitioners seeking to use these advanced methodologies. The findings underscore the versatility and efficiency of PINNs, enhancing a deeper understanding of their role in advancing geoscientific research and applications. Ultimately, this review aims to bridge the current knowledge gap, promote the wider adoption and development of PINNs in geosciences, drive innovation, and enhance the accuracy and reliability of geoscientific models and predictions.

Granite-related uranium deposits constitute a significant uranium resource in South China. This study focuses on the ore-controlling structural characteristics of five major uranium ore fields (Motianling, Miaoershan, Taoshan, Zhuguangshan, and Lujing) within the region’s two principal granite-related uranium belts, the Chenzhou-Qinzhou and Taoshan-Zhuguangshan belts. Uranium-bearing granites in South China are emplaced episodically from the Early Triassic to Late Cretaceous (250–80 Ma), with predominant magmatism occurring in the Triassic and Jurassic. Crucially, uranium mineralization is temporally linked to specific tectonic stages. The most significant regional uranium mineralization occurred during the Late Yanshanian (Late Cretaceous, 100–65 Ma) in an extensional tectonic regime, forming large deposits within composite plutons such as Taoshan, Zhuguang, and Guidong. The research highlights that understanding the temporal sequence and spatial response of uranium mineralization to specific tectonic stages within the broader tectonic evolution is crucial. Specifically, our findings demonstrate that the most significant uranium mineralization occurs during distinct extensional phases (e.g., Late Yanshanian), subsequent to granite emplacement and is structurally controlled within favorable settings of composite plutons. Graphical abstract

Research to determine the shallow geological structure features was carried out in Balikpapan Bay, East Kalimantan Province, using the sub-bottom profiling (SBP) method. The aim is to contribute marine geological data for regional development. Some geological structures are observed at the bottom of Balikpapan Bay based on the analyses of SBP records. These features are interpreted as representing and as the continuation of terrestrial geological data and information surrounding Balikpapan Bay obtained from the Geological Agency. The observed geological structures in the bay are bedding planes, folds, thrusts, and strike-slip faults. The geological structures observed in Balikpapan Bay are due to compressional stress derived from the Makassar Strait spreading center. These results support Terrestrial Geological Agency data surrounding the bay that occurred in the marine environment. It is hoped that the results of this research will become supporting data when a bridge over Balikpapan Bay will be built in the future which will connect Balikpapan City to the new National Capital. The development of shallow geological structures in Balikpapan Bay is influenced by a branch of the regional Adang Fault passing through the bay. This strike-slip fault manifests itself as many shallow geological structures as observed in SBP records. Spreading center in the middle of Makassar Strait that generates compressional forces was causing the development of such structures in either Balikpapan Bay or its terrestrial areas.

Northeastern Tibet, as a core component of the Central China Orogenic Belt, preserves critical evolutionary records of the Proto-Tethys and Paleo-Tethys Oceans, offering significant insights into plate reconstruction, paleontology, and paleoclimate research. It comprises three major orogenic systems – the East Kunlun, North Qaidam, and Qilian orogens – with multiple sutures reflecting complex geological formation processes. While a consensus exists that the Proto-Tethyan basin closed during the Late Ordovician to Early Silurian, leading to the amalgamation of the Qaidam, Olongbuluke, and Qilian blocks, debates persist regarding the tectonic affinities of these blocks and the evolutionary relationship between the Proto-Tethys and Paleo-Tethys Oceans. By integrating published geological records and paleomagnetic datasets, this study investigates the tectonic affinities of these microcontinents and reveals distinct pre-Paleozoic tectonic histories: the Qaidam Block, characterized by a Meso-Neoproterozoic basement, exhibits affinity to the Rodinia supercontinent; the Olongbuluke Block shows close linkages with the North China Block, supported by shared paleomagnetic latitudes and detrital zircon age distributions; and the Central Qilian Block is genetically connected to the South China Block, as evidenced by magmatic-isotopic signatures and detrital zircon data. This study challenges the traditional view that the Kunlun Ocean represented a Precambrian oceanic basin persisting until the Late Triassic–Early Jurassic closure, instead proposing its closure occurred during the Late Silurian–Early Devonian. Additionally, paleomagnetic and detrital zircon data suggest that the Proto-Tethys Ocean closure may be genetically linked to the assembly of the Gondwana supercontinent, with subsequent fragmentation of Gondwana triggered by the opening of the Paleo-Tethys Ocean.

Mineral deposits are a globally important resource. However, the supply of shallow minerals is close to depletion, forcing exploration activities to expand to deeper areas. Deep exploration has greater challenges compared to shallow exploration, and how to effectively extract the intrinsic connection between exploration data and deep concealment, and how to quickly and accurately locate target zones, remain urgent challenges to be solved. Mineral prediction and geophysical inversion are the core links in mineral exploration, and how to make up for the shortcomings of traditional methods in these links has become an important topic of current research. In the past decade, with the wide application of big data technology in the field of geological prospecting, more and more geological data have provided support for the application of machine learning (ML) in geophysical exploration and mineral prediction. ML overcomes the limitations of traditional methods to a certain extent, such as reducing human subjectivity and improving the ability to mine the laws among geological data, showing great potential. This study summarizes the progress of the application of ML, especially deep learning, in the field of mineral exploration in recent years, focuses on the two key aspects of geophysical inversion and mineral prediction, analyzes the advantages and limitations of the various methods, and makes concluding comments on the future direction of development, with the aim of providing valuable references for the on-site application of ML in mineral exploration and the direction of future research.

The demographic potential of the settlements in the Visok micro-region has been significantly disrupted since 1948. The latest census (2022) shows that the Visok micro-region now consists of very small settlements, all with fewer than 100 inhabitants. In all 22 villages of the Visok micro-region, there are only 504 inhabitants, with unfavorable age structures. The region’s average age has remained above 65 for the last 30 years, significantly older than the surrounding areas. Low salaries, bad infrastructure, and the industrialization of Pirot City represent the main reasons for depopulation. The Zavoj Lake catastrophe caused three villages to be submerged, which accelerated depopulation even more. Most of the remaining inhabitants are engaged in farming or work in the construction, stone, and forestry industries, with sheep farming being a notable tradition. The local population shows strong ecological awareness, advocating for industries that align with nature, creating an ideal environment for nature enthusiasts and scientists. The only way for revitalization is to have organized seasonal migrations based on the summer tourist season. The main aim for revitalization would be a national-level plan for creating a brand of untouched nature, with traditional agricultural products for the complete experience.

This article investigates the application of a model for calculating sustainable development goal indicator 11.7.1 using the example of Podgorica (Montenegro). Indicator 11.7.1 measures the proportion of open public spaces in relation to the total built-up area of the city, which is a key indicator of quality of life, social integration, and the sustainability of urban areas. Utilizing a methodology based on geographic information systems (GIS) analysis and detailed spatial planning documentation, data on various categories of public and green spaces, as well as street areas, were collected and analyzed. The GIS analysis model enables precise mapping and digitization of areas, while spatial planning documents provide necessary data for verification and compilation. The total area of open public spaces, including parks, squares, green areas, and streets, was calculated to be 1,028 ha. In comparison with the total built-up area of the city, which is 858.49 ha, the proportion of open public spaces is 119.75%. The results of this study highlight the importance of monitoring and improving public spaces for the sustainable development of urban environments. The proposed GIS analysis model and spatial planning methodology can serve as a basis for future research and application in other urban areas, enabling more accurate monitoring and improvement of citizens’ quality of life.

This study analyzes the spatiotemporal patterns of seven common cancers in the male population using 23 years of data (1999–2021) across 18 counties in Central Serbia. The spatial distribution of cancer incidence and mortality rates and their temporal evolution were examined at the county level using Getis–Ord G i * {G}_{i}^{* } techniques, while trends were analysed with Mann-Kendall statistics. Hot spot analysis revealed a decline in mortality rates, whereas incidence rates increased. New colorectal cancer incidence hotspots emerged in three counties, while consecutive bladder cancer hotspots appeared in two counties. Conversely, new cold spots in mortality rates were found for lung and bronchus cancer in four counties, stomach cancer in two, and laryngeal cancer in one. An increasing trend in both incidence and mortality rates was identified for lung and bronchus cancer and colorectal cancer in three counties. In addition, prostate and pancreatic cancer incidence and mortality rates rose in two counties. In contrast, stomach cancer showed a decreasing trend in four counties, as did laryngeal cancer in two. This research enhances public health efforts by identifying vulnerable areas, assessing current health strategies, and guiding new interventions.

The European green agenda aims to preserve the environment and climate, reduce CO 2 emissions, and replace fossil fuels with renewable energy. It mostly relies on electric vehicles, energy storage, solar, and wind power plants. It requires an order of magnitude higher amount of critical minerals (In this text, the term minerals is often used for individual chemical elements, although it is common to refer to combinations of chemical elements that have a corresponding chemical composition, crystallization, and name.), poorly represented in the lithosphere, with problematic recycling, with extraction requiring considerable amounts of energy, fossil fuels and causing unacceptable damage to people and nature in countries that supply raw materials. Rising global average temperatures cast doubt on the overall effects of decarbonisation. The time frame of profit-oriented planning is too short and cannot respect the dynamics of the energy sector. Together with market uncertainty, regulations, and incentives did not encourage investors to take all the steps we had hoped for. The long-term needs and availability of key minerals are considered together with an overview of the financial and environmental conditions offered to the population in the countries where mining is carried out. Growing popular resistance to cheap and environmentally damaging mining and increasing demand for critical minerals may call into question the sustainability of current practices. The development of new technologies should be geared towards solutions that use abundant minerals in the lithosphere, while long-term sustainability requires, within a much-desired paradigm shift, that fair conditions be offered to the population of countries that supply critical minerals. The main objective of this article is to use scientifically based considerations to identify the key issues of the title topic, to assess this complex and multidisciplinary subject, and to draw feasible conclusions and recommendations. Graphical Abstract Coltan mine in Rubaya, Democratic Republic of Congo (taken from WikiPedia, https://en.wikipedia.org/wiki/Rubaya_mines)

The comprehensive analysis of spatiotemporal variations in water quality is crucial for ecosystem management. This study analyses and maps spatiotemporal variations in water quality at Vrana Lake, a coastal shallow lake in Dalmatia, Croatia. We established a monitoring grid of 20 stations and conducted monthly in situ measurements of seven water quality parameters from July 2023 to June 2024 using multiparameter probe. We measured electrical conductivity, turbidity, salinity, water temperature, dissolved oxygen, oxygen saturation, and chlorophyll-a. We analysed the correlation between these parameters, water level, and meteorological factors over a year and the impact of climate change over the 34 years. Additionally, we evaluated 15 geographic information system (GIS) spatial interpolation methods for mapping the distribution of water quality parameters, using root mean square error (RMSE) and mean error (ME) metrics. The vertical stratification analysis revealed that the lake’s shallow nature allows effective assessment through median values. Key findings highlighted that air temperature, precipitation, and wind significantly affect water quality dynamics. The Simple Kriging – Trend emerged as the best GIS spatial interpolation method for modelling water quality parameters. Overall, this study enhances the understanding of water quality variations and their implications for ecosystem health in coastal shallow lakes. Graphical abstract

This study explores how long-term surface coal exploitation has affected the distribution of economic functions and contributed to hierarchical changes within the settlement network of the municipality of Stanari. The aim of this study is to contribute to a broader understanding of the spatial consequences of intensive resource exploitation on settlement systems. Using the case study of Stanari, the analysis focuses on changes resulting from ongoing mining activities. The most pronounced transformations occurred in settlements near the mine, where exploitation led to increased employment in industry and services, while more distant settlements retained their functional type with minor modifications. Centrality analysis indicates shifts in the hierarchical position of settlements. The geographic information system was utilized as a key analytical tool for spatial modeling and visualization of functional transformations within the settlement network, providing deeper insight into the spatial-functional changes caused by coal exploitation. To assess future transformation, a survey was conducted, examining the relationship between place of residence and attitudes toward potential relocation and employment. The results show a statistically significant association between these decisions and respondents’ spatial affiliation, suggesting possible future changes in the functional structure and hierarchy of the settlement network. The findings of this study offer a broader contribution to the understanding of spatial and functional dynamics in areas affected by intensive resource exploitation. They provide important insights for planning strategies aimed at supporting the local population and guiding decision-making processes in Stanari and similar regions, which may be valuable to local governments and relevant ministries, by informing spatial planning and management of resource exploitation. Furthermore, the study highlights the importance of employment dynamics and workforce retention as critical factors for ensuring both demographic and economic sustainability in areas affected by mining-induced transformations. The ultimate goal remains maintaining demographic stability and preserving the long-term functional integrity of the settlement network.

Hypertension is one of the most prevalent cardiovascular diseases globally. This study presents a spatio-temporal analysis of hypertension among patients treated at the Institute for Cardiovascular Diseases of Vojvodina between 2000 and 2023. A Mann–Kendall trend analysis was performed at the settlement level using Python to identify trends in hypertension cases. The Getis-Ord Gi* statistic, implemented through the Emerging Hot Spot Analysis (HSA) tool in ArcGIS Pro, was applied to detect spatial clusters of hot and cold spots. The Mann–Kendall analysis identified significant trends in 40 of the 467 settlements in Autonomous Province Vojvodina. Of these, 36 settlements exhibited an increasing trend in hypertension cases, while a decreasing trend was observed in 4 settlements. The Emerging HSA detected 19 clusters classified as “Consecutive Hot Spots” among 316 analysed clusters, with no discernible patterns observed in the remaining clusters. To compare the findings and results from these two analyses, the Kriging method was applied to the z -values of trends from both analyses. The method indicated higher trends in hypertension cases in settlements within South Bačka County. This research identifies areas with significant hypertension trends, helping to pinpoint high-risk regions. The results support further investigation into the causes and guide targeted public health interventions in the study area.

This study examines regional mortality trends in Montenegro over the past three decades (1991–2019), focusing on the three leading causes of death: circulatory diseases, neoplasms, and ill-defined conditions. Mortality data were obtained from the Statistical Office of Montenegro (MONSTAT) and the Institute of Public Health of Montenegro. Analysis was performed using crude death rate by cause of death for three regions in Montenegro. Health (doctors and hospital beds per 10,000 inhabitants) and socio-economic indicators (GINI coefficient and Human Development Index) were sourced from the World Health Organization database. A structured three-stage analytical approach was employed to investigate regional patterns and determinants of cause-specific mortality: descriptive statistics and trend analysis using linear regression, t -test, and multiple linear regression. Circulatory diseases account for the highest proportion of deaths (42%), followed by neoplasms (17%) and ill-defined causes (15%), with all other causes contributing to the remaining 25%. The analysis reveals significant and persistent regional disparities in cause-specific mortality, with south–north gradient in mortality observed for circulatory diseases and neoplasms, while ill-defined conditions displayed a reversed north–south pattern. These patterns have remained relatively stable over time. Furthermore, multiple linear regression analysis underscores the influence of health and socio-economic factors on regional mortality variation. The findings highlight the urgent need for spatially targeted public health interventions, particularly those aimed at addressing noncommunicable disease risk factors. Integrating geographic and public health approaches offers a critical pathway for reducing mortality disparities and improving population health outcomes in Montenegro.

The subject of this research is the analysis of flood dynamics in the Ukrina River Basin, Bosnia and Herzegovina, using a remote sensing-based approach and geographic information systems during the period 2016–2019. The aim is to identify the spatial and temporal patterns of floods by integrating satellite-derived precipitation data, hydrological data, and Sentinel-1 imagery processed through Google Earth Engine. The methodology included the use of CHIRPS precipitation data and the Standardized Precipitation Index (SPI) for identifying meteorological anomalies, while Sentinel-1 SAR data were used to map flood extent based on radar backscatter change detection. The approach combined temporal analysis with spatial overlays of land use and administrative boundaries to assess affected areas. Flood events were identified in January 2016 (89.98 ha), March 2017 (179.85 ha), March 2018 (58.81 ha), and May 2019 (195.38 ha), coinciding with periods of above-average precipitation (>125%), positive SPI values, and elevated water levels. The spatial analysis of flooded areas, overlaid with land use data, revealed that agricultural land was the most affected category, with 79.21 ha flooded in 2016, 169.15 ha in 2017, 48.89 ha in 2018, and 184.90 ha in 2019. Built-up areas were also significantly impacted, posing risks to infrastructure and economic stability. The cities and municipalities of Derventa, Prnjavor, and Stanari were most frequently affected by floods during the study period. The findings highlight the role of cumulative precipitation and hydrological conditions in triggering flood events and provide insights for flood risk management, including adaptive strategies, early warning, and sustainable land use planning.

Flash floods are the result of climatic and hydrological extremes and are manifested by dynamic and complex processes of movement of water and sediment. They represent the most frequent and widespread natural disaster at the global level, with unwanted ecological and economic consequences. The main causes of flash floods are related to numerous meteorological and physical–geographical factors. In the territory of Serbia, flash floods represent the most common natural risk with serious consequences for people’s lives and activities. Flash flood susceptibility mapping plays a crucial role in flood risk assessment and management. The current study prepared a flood inventory using light detection and ranging (LiDAR) derived digital elevation model, and it used integrated tree machine learning models (random forest [RF], classification and regression trees [CART], and support vector machine [SVM]) to predict flood susceptibility in the Ljuboviđa watershed, municipality Ljubovija, western Serbia. First, 12 independent variables were employed as conditioning factors: lithology, rainfall, land use/cover, elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power index, distance from streams, distance from roads, and normalized vegetation index. Using the well-known scikit-learn (train_test_split) Python module, the flood inventory dataset was split into 70 and 30% for training and validation, respectively. The models’ performance was additionally assessed using the area under the curve (AUC). The results of the accuracy assessment demonstrated that the models for predicting flood susceptibility, RF, CART, and SVM, had AUC values of 0.854, 0.802, and 0.831, respectively; it means that RF had 85.4%, CART 80.2%, and SVM 83.1% chance of correctly ranking a random positive example higher than a random negative example, which represents the predictive power of the used models. When it came to predicting flood susceptibility, the RF model outperformed the other models used. This model estimates that 15.49, 16.04, 15.67, 23.10, and 29.70% of the watershed are very low, low, moderate, high, and extremely highly susceptible to floods, respectively. Thus, our study shows that data produced from LiDAR is potentially helpful in managing flood risk, particularly when assessing flood-related issues in the future. Flash-flood susceptibility maps have become a vital tool for risk prevention and management for government and local authorities (particularly national and local civil protection agencies, urban planning and land management departments, Ministries of Water and Environment), emergency response services (police, fire, and medical services), infrastructure and utilities sectors, insurance companies, and others. Graphical abstract

The Banjska Monastery is a medieval monastery in Serbia of great cultural and spiritual importance to the Serbian population, as it served as the endowment and burial church of King Milutin, a powerful Serbian ruler of the fourteenth century. This article aims to assess the geoheritage value of the Banjska Monastery, located near Zvečan, using the Urban Geoheritage Assessment Model (UGAM), and to explore its potential for geotourism development by converting its heritage value into tourism value. To support UGAM parameters (such as educational potential, geoheritage diversity, and paleontological significance), macroscopic observations of the building stones were conducted, along with petrological analyses of five samples collected from the monastery and micropaleontological investigations of three limestone samples. These analyses aimed to identify microfacies, composition, structural characteristics, and sedimentary environments. The investigation revealed two rock types: sedimentary rocks (limestone, sandstone, and tufa) and metamorphic rocks (marble). UGAM results revealed high scores for scientific and natural value, confirming the site’s strong potential for urban geotourism development. However, the model also highlighted deficiencies in tourist infrastructure, particularly in information boards, guided tours, and visitor centers. These findings may assist local policymakers and stakeholders in converting natural heritage values into tourism assets.

Groundwater forms through infiltration beneath the Earth’s surface as it moves through various rock layers. Reliable and sustainable water supplies are predominantly sourced from underground sources, making them some of the most important natural resources. The aim of this study is to assess and identify potential groundwater zones using Geographic Information Systems, remote sensing, and multi-criteria analysis, specifically the Analytical Hierarchy Process (AHP), within the Kolubara Mining Basin influence zone in Serbia. This study considers factors such as rainfall, geology, slope, river network density, and land use/land cover. These factors were evaluated using the Saaty scale to determine their respective weights. Due to their higher weights, the AHP process revealed that precipitation and geology are the most influential factors in groundwater formation. Groundwater potential zones are categorized into five classes: very low, low, moderate, high, and very high. These results will enhance groundwater management at both regional and local levels and contribute to the future protection of this resource.

The aim of study is to analyze the agri-geographical transformation of rural settlements on changes in the intensity of soil erosion and the geospatial differentiation of soil erosion intensity according to the specificities of rural settlements. The study area included 349 rural settlements in 14 municipalities of Central Serbia. The shift-share analysis method was applied to identify four types of rural settlements: progressive, stagnant, regressive, and dominantly regressive types. An erosion potential model was used to estimate the soil erosion in 1971 and 2011. Cluster analysis (CA) was used to differentiate rural areas of municipalities based on selected indicators. The highest rates of soil erosion intensity reduction were found in the regressive and dominantly regressive types (average specific gross erosion 1971: Ws 1 = 980 m 3 /km 2 /year; 2011: Ws 2 = 587 m 3 /km 2 /year). The lowest reduction in erosion intensity was observed in the progressive and stagnant types (average specific gross erosion in 1971: Ws 1 = 1,214 m 3 /km 2 /year; 2011: Ws 2 = 936 m 3 /km 2 /year). The spatial differentiation of municipalities through CA revealed a clear pattern: the risk of soil erosion decreases along a north-south gradient. Also, the results of CA showed that natural conditions (forest cover, mean altitude, sediments of Neogene) are the most significant in the differentiation of the rural space of Central Serbia. The multidisciplinary approach used in this study enables a holistic understanding of erosion processes, supports the identification of spatial patterns, and facilitates the formulation of targeted and region-specific land management strategies.

This study explores the cultural value of geodiversity through the religious significance of the landscape in Gornje Podunavlje, northwest Serbia. Focusing on the veneration of Saint John of Nepomuk – the patron saint against water hazards – we investigate how his statues reflect historical community responses to flood risks in the Bačka region. Using nineteenth-century maps, geographic information systems (GIS), and geospatial analyses were applied to map statue locations, digitize historical Danube courses, and examine the spatial relationship between statues, settlements, and geomorphological features. Hypsometric and floodplain mapping, along with Euclidean distance and flood risk assessments (Danube Flood Risk Management Plan – DFRMP project data), revealed that all statues in Gornje Podunavlje are located on higher ground at the edges of settlements and oriented toward the Danube. In contrast, statues in other Bačka settlements lack this geomorphological alignment. These patterns suggest a culturally embedded adaptation to flood hazards. Present-day settlement outlines were digitized using satellite imagery to confirm current statue placements. The findings demonstrate how physical geography has influenced religious expression and settlement planning. This research emphasizes the importance of interpreting geodiversity not just as a physical attribute but as a cultural and historical factor shaping human–environment interactions in flood-prone flatland regions like Bačka (northwestern part of Serbia). Graphical abstract

This review explores the application of local climate zones (LCZs) through various measurement methods, with a focus on mobile and in situ data collection in European urban environments. The selection of articles for review was guided by specific keywords and a structured literature search process. In total, 61 studies were included for analysis. These studies primarily used in situ and mobile measurement techniques (car, bicycle, wearable device), while some also incorporated remote sensing technologies and machine learning. The reviewed papers addressed a range of urban climate topics, including urban heat island effects, outdoor thermal comfort, air temperature variations, and others, while using various approaches to distinguish between LCZs within cities. While in situ measurements offer consistent and reliable data, their spatial limitations often fail to capture detailed microclimatic differences in dense urban settings. On the contrary, mobile measurements provide improved spatial resolution and adaptability. Integrating multiple data sources is crucial to enhance our understanding of urban microclimates and thermal behavior across different LCZ classifications.

This study assesses the water quality of the upper course of the Krivaja River in northern Serbia, part of the Tisza and Danube catchments. The analysis focuses on a 2 km stretch with the highest pollution levels, using monitoring data from 2014 to 2017 and synthetic surface water pollution indicators. A total of 96 samples were collected at 6 different surface water sampling points, 16 per site. Wastewater were collected from 4 sampling points, 16 before its inflow into the Krivaja River and 32 as effluent. Surface water sampling where taken in accordance with SRPS EN ISO 5667-3:2007, and wastewater with SRPS ISO 5667-10: 2007 methods. Analyses of metals in water samples were carried out by atomic absorption spectroscopy, and nitrogen and phosphorus compounds were measured by UV spectrophotometry. Comparison with national standards revealed extremely high pollution levels: COD exceeded limits by 43 times, BOD 5 by 76 times, total nitrogen by 23 times, and N-NH 3 by 211 times. Nickel concentrations also surpassed the European Union Directive 2013/39/EU limits. The Nemerow index values (27.6–102) highlighted heavily polluted discharge sources, such as animal feed production. Cluster analysis confirmed these findings. Key pollutants include organic indicators (COD, BOD 5 , KMnO 4 ), nutrients (NO 2 − , NO 3 − , TN, N-NH 3 ), TSS, Cl − , and Fe. Metals (Mn, Zn, Ni, Pb, Cu, Cr, Cd, As) showed minor variations, minimally impacting the overall score. Poultry wastewater exhibited significant quality fluctuations, ranging from moderately to heavily polluted (PIW = 4.05–8.20). The primary cause of poor water quality is inadequate wastewater treatment in the region. Urgent measures are needed to mitigate pollution and restore the Krivaja River’s ecological integrity. Graphical abstract

This preliminary study examines the seasonal distribution and correlation of negative air ionization near waterfalls situated on two types of geological substrates, and its relationship to water properties such as electrical conductivity (EC), total dissolved solids (TDS), salinity, and water temperature. Measurements were conducted between 2022 and 2024 at six waterfalls in the Stara Planina Mountain. Five waterfalls, Tupavica, Draganov Vir, Tri Kladenca, Donji Piljski, and Grujin, are located on Triassic and Permian sandstones. The sixth, Bigar waterfall, is located on limestone and exhibits characteristics typical of waterfalls formed on carbonate substrates. The results indicate that the underlying geology significantly influences variations in airborne anion concentrations. Waterfalls on sandstone substrates exhibit much higher levels of negative ionization compared to those on limestone. This contrast appears to be linked to water chemistry: sandstone-based waterfalls tend to have lower values of dissolved solids and EC, whereas limestone waterfalls display higher concentrations. These findings contribute to understanding the “waterfall effect” and highlight how geological and environmental factors shape the generation of natural air ionization. Such insights can inform the selection of natural sites for eco-tourism, wellness retreats, and health-oriented infrastructure where exposure to high negative ion concentrations is beneficial.

Northwest Serbia has long been recognized for its rare and abundant mineral resources. A rare source of fluvial tin cassiterite ore has been identified in streams along the southern slopes of Cer Mountain. This tin deposit was first exploited during the Bronze Age, around the mid-second millennium BCE (Before the Common Era). Several surface-level copper sources in the Podrinje–Valjevo mountain range were known, with evidence suggesting that their exploitation began as early as the fourth millennium BCE. Archaeological surveys and excavations have uncovered numerous Late Bronze Age necropolises, while evidence of only a single lowland settlement from this period has been found. However, research has also revealed a significant number of hillforts from the Transitional period, marking their first appearance in this region. Material culture, along with a series of calibrated radiocarbon dates, points to the presence of culture originating farther to the north in southern Pannonia. These hillforts were all strategically located on the highest mountain peaks and were fortified with massive walls and defensive ditches clear indicators of a substantial transformation in landscape use between approximately 1000 and 850 BCE. Further analysis using Light Detection and Ranging technology uncovered the immense scale and architectural complexity of the Cikote stronghold, a scale unprecedented in earlier periods. In addition, a Geographic Information System-based viewshed analysis of 14 hillforts and lowland settlements from this era demonstrated that all sites were visually connected to at least one neighbouring location. Some strongholds maintained visual links with multiple sites, with lines of sight extending over several dozen kilometres.

This study aims to evaluate the significance of glacial lakes within the municipality of Plav from the perspective of geotourism development, employing the modified geosite assessment model methodology for geosite assessment. Although the Plav region is distinguished by its exceptional natural values and landscape diversity, it has not yet been the subject of systematic investigation concerning its geoheritage and geotourism potential. This research represents an initial contribution toward the identification, valorization, and promotion of glacial geosites as a foundation for the development of sustainable geotourism in the area. The analysis encompasses four glacial lakes: Lake Plav, Visitor Lake, Hrid Lake, and Abdija Lake. These lakes represent attractive geosites and play a vital complementary role in shaping an integrated geotourism offer, contributing to the diversity of content and spatial cohesion of tourist attractions within the municipality. The findings confirm that the glacial lakes in Plav possess considerable geotourism potential, providing a robust basis for the planning of specialized tours, educational activities, and the dissemination of geoscientific knowledge. Realizing this potential necessitates coordinated collaboration among local government bodies, tourism organizations, and other relevant stakeholders to develop infrastructural support, enhance the interpretation of geosites, and promote geotourism as a sustainable form of tourism. Although previously underrecognized within the geoheritage framework, the Plav area offers substantial opportunities for further scientific research, educational initiatives, geoconservation efforts, and recreational use in alignment with the principles of geotourism development.

Flash floods, which can increase in severity and frequency due to climate and land-use changes, are among the most destructive natural hazards globally, causing significant damage and loss of life. This study employs the Flash Flood Potential Index (FFPI), a parametric model based on terrain and land surface characteristics, to assess flood-prone areas in North Macedonia’s catchments, utilizing Geographic Information Systems and advanced geospatial analytics. The average FFPI values, which typically ranges from low values indicating minimal risk to higher values indicating greater flash flood potential, show values from 1.9 to 9.2 across North Macedonia, with an average of 6.0. Within the main and subcatchments, the FFPI averages vary from 3.9 to 7.2. Based on these results, 24.7% of the subcatchments are identified as very highly susceptible to flash floods (torrential catchments), indicating significant spatial variability in flood risk across the country. The model classifies susceptibility into five categories, from 1 (very low) to 5 (very high). Nationwide, 25.6% of the area is categorized as highly susceptible to flash floods. The FFPI model was validated using flash flood event records and corresponding intense precipitation data. Validation using the receiver operating characteristic-area under the curve method yielded a value of 78.2%, reflecting the model’s high accuracy in identifying flash flood-prone areas. This study contributes to sustainability by providing a scientific basis for resilient land-use planning, flood mitigation strategies, and climate change adaptation. By integrating factors such as slope, lithology, land use, vegetation, and the bare soil index, alongside rainfall, peak discharges, and response times, this research offers critical insights for policymakers and urban planners to enhance flood risk management and promote sustainable development.

This study integrates the Soil and Water Assessment Tool (SWAT) modelling with multi-criteria decision-making (MCDM) analysis to assess and evaluate the spatial suitability for small hydropower development. The SWAT model is employed to perform a comprehensive hydrological analysis of the study area, which enables a precise characterisation of the hydrological dynamics within the catchment, ensuring that the derived data accurately reflects the physical realities of the region, which, in turn, enhances the reliability of subsequent spatial analyses. The outputs from the SWAT analysis are subsequently utilised as critical inputs for the MCDM analysis. This analysis integrated various spatial and environmental factors, including terrain slope, soil type, land cover, proximity to existing infrastructure, and potential ecological impacts. Integrating these diverse datasets allows the development of comprehensive grid and vector databases and maps delineating areas within the catchment most suitable for small hydropower development. These maps identify optimal locations and balance ecological sensitivity with socio-economic considerations, ensuring that growth is sustainable and beneficial to local communities. The findings from this integrated approach provide valuable insights for regional planners and policymakers, offering a scientifically grounded basis for the strategic development of small hydropower projects. This research significantly contributes to advancing spatial planning methodologies in Serbia, highlighting the importance of integrating advanced hydrological modelling with sophisticated decision-support tools in contemporary environmental management and infrastructure development.

This study examines residents’ perceptions of environmental and disaster risks near the “Duboko” regional landfill in Serbia. It aims to uncover factors that affect public trust and community resilience. This approach is rooted in interdisciplinary perspectives on risk perception, institutional trust, and socio-ecological resilience. A structured, self-administered questionnaire was used to collect data from a stratified sample of 1,180 respondents across nine municipalities in Western Serbia. The statistical analysis involved descriptive statistics, t -tests, ANOVA, Pearson correlation, and multiple linear regression. The regression models indicated that gender, education level, type of settlement, property ownership, and household size were significant predictors across various thematic dimensions. The model addressing risk perception and environmental impact accounted for 5.6% of the variance, while the model regarding institutional trust and transparency explained 7.4% of the variance. Higher perceived risk and lower institutional trust were found among women, individuals with lower levels of education, rural residents, and respondents from smaller households. Furthermore, increased transparency and access to environmental information correlated with enhanced trust and perceived resilience. These results emphasise the complex nature of landfill risk perception and highlight the need for participatory communication, transparent governance, and context-sensitive community involvement to bolster disaster resilience and public health protection in areas impacted by landfills.