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Geothermal resource potential assessment of Erdaobaihe, Changbaishan volcanic field: Constraints from geophysics

  • Zhi-He Xu , Zhen-Jun Sun EMAIL logo , Wei Xin and Liping Zhong
Published/Copyright: September 20, 2021
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Abstract

Geothermal resources occurring in the Changbaishan volcanic field are directly or indirectly controlled by volcanic activity and exhibit a close correlation with deep-seated faults. Energy and thermal transfer are generally controlled by groundwater circulation and hot gas emission. This article considers the detectability of hot water and gas by geophysical methods. The controlled source acoustic magnetotelluric (CSAMT) and radon (222Rn) gas methods give straightforward information on electrical resistivity and natural radon emissions, respectively, to assess the geothermal condition. The CSAMT method detected five-banded low-apparent resistivity bodies (decreasing from 3,000 to 300 Ωm), indicating that there exists a high degree of water-bearing capacities in the subsurface. The radon (222Rn) gas concentrations were monitored in two rapid growth zones: one zone showing values ranging from 3,000 to 23,000 Bq/m3, and the other with values from 4,000 to 24,000 Bq/m3. These changes demonstrate that the heat energies available in these areas were very high and that there is potential for geothermal resources in those zones. Combining with previously published data from geothermometry and geothermal drilling, we argue that there is great potential in Erdaobaihe for geothermal exploitation and that the geothermal resource type should be classified into uplift mountain geothermal system no magma type.

1 Introduction

Geothermal resources, as an alternative source of energy, are generally developed in areas where volcanism and magmatism have been active since the Late Cenozoic (i.e., the Jingbohu, Wudalianchi, and Tengchong geothermal fields) [1,2]. As one of three active volcanoes in China, the Changbaishan volcanic field saw eruptions in 1668, 1702, and 1903 AD, which were high-intensity explosive eruptions and demonstrated favorable geothermal resource potential [3]. The location of each hot spot in the Changbaishan geothermal field (CHGF) exhibits a close correlation with the fault that transfers the geothermal flow from the deep magma chamber to shallower depths [4,5]. Therefore, assessing the geothermal resource potential of the periphery of the CHGF is of great possible value.

However, the majority of efforts in this area to exploit geothermal resources are around the Changbaishan volcano (e.g., Lake Shoreline, Julong, and Jinjiang geothermal fields). There is not even a hot spring with high economic value and exploitation potential further away from the Changbaishan volcano, which is inconsistent with the classical volcano-geothermal model [3]. This is due to the periphery of Tianchi caldera lacking geophysical data covering the deep faults. In contrast to geothermal drilling, geophysical methods not only acquire in situ information but also allows for the investigation of the outer ring of the geothermal active zones [6]. Controlling factors in the geothermal zone around active volcanoes are mainly associated with faults, magma chambers, and the distribution of subsurface water. The application of multiparametric geophysical methods can provide adequate information to delineate the complicated geological bodies at different depths. Moreover, Erdaobaihe located at the foot of the Changbaishan, 25 km north of the Tianchi caldera, enjoys favorable advantages in terms of location and geological conditions. Therefore, in this work we present results from surveys undertaken using the advanced controlled source audio-frequency magnetotellurics (CSAMT) method, which displays great depth penetration, to uncover the migration pathways of hot water and the radon (222Rn) gas method to assess the heat energy and to confirm the inferences made from the analysis of the CSAMT data.

2 Geological setting

2.1 Regional setting

The CHGF is located between the inland of the Eurasian Plate and the Pacific subduction zone [7]. There are three polygenetic volcanoes dominated by basaltic to silicic volcanic rock suites: Tianchi, Wangtian’e, and Namphothe. More than 200 monogenetic volcanoes that erupt basaltic products are sporadically scattered around these polygenetic volcanoes [8]. During the Late Mesozoic to Cenozoic, the tectonic evolution of the CHGF was dominated by the subduction of the Pacific plate with the products of volcanic activity (e.g., volcanic cones, basalts lava plateau, ash, pumice, and pyroclastic flows) being prominent in the Eurasian plate, particularly in the CHGF [9,10] (Figure 1). The regional fractures of the CHGF are dominated by north-east (NE), north-west (NW), and north-south (NS) trending faults [11]. These faults can be categorized into two types: (1) the NW and NE older faults, which are suggested to be related to the successive subduction of the Pacific plates in the eastern margin of the Eurasian continent; and (2) the NS young faults, which are associated with Late Cenozoic volcanic activity and form the Julong hot spring.

Figure 1 
                  Simplified map showing the location of the CHGF within the context of the continental intraplate framework. The red lines show the movement direction of the Pacific and Philippine Sea (PHS) plates. The black dotted lines represent the present boundary of the subducting of the Pacific slab [12].
Figure 1

Simplified map showing the location of the CHGF within the context of the continental intraplate framework. The red lines show the movement direction of the Pacific and Philippine Sea (PHS) plates. The black dotted lines represent the present boundary of the subducting of the Pacific slab [12].

Medium-temperature geothermal resources are generally clustered around the Tianchi caldera or in the valleys around the cone, such as the Hubin, Julong, and Jinjiang hot springs. Low-temperature geothermal resources are scattered further away from the Tianchi caldera, such as Erdaobaihe and Songjianghe [14]. The formation of these hot springs is a consequence of the continuous advection of heat and groundwater from the activation of the Changbaishan volcano [15] (Figure 2).

Figure 2 
                  Simplified regional geological map of the CHGF (modified from Qian et al., 2014 [13]), showing the locations of hot springs. Typical localities of hot springs are labeled as follows: (A) Hubin, (B) Julong, and (C) Jinjiang.
Figure 2

Simplified regional geological map of the CHGF (modified from Qian et al., 2014 [13]), showing the locations of hot springs. Typical localities of hot springs are labeled as follows: (A) Hubin, (B) Julong, and (C) Jinjiang.

2.2 Local setting

The Cenozoic Manjiang formation is widely distributed around Erdaobaihe and the Junjianshan formation is locally exposed on the western side. The former’s lithology is amygdaloidal basalt and the latter’s is massive black basalt. The Mesoproterozoic Daxing formation occurs sporadically in the center of the GHGF and is mainly composed of quartz sandstone, sericite quartz sandstone, chlorite, and epidote. The Mesoproterozoic Wangdeshan formation is exposed in the northern and southern sides, and the strata are composed of dolomitic marble, altered marble, and brecciated marble. The Late Permian intrusion occurs sporadically around the eastern side and the lithology is medium fine-grained monzogranite. Due to the sporadic exposure and widespread Quaternary basalts, the extent of the Tianchi-Erdaobaihe fault is difficult to identify. Three ascending springs were discovered in the middle of the geophysical profile (Figure 3).

Figure 3 
                  Simplified geological map of Erdaobaihe, showing the locations of CSAMT and radon gas samplings (unpublished).
Figure 3

Simplified geological map of Erdaobaihe, showing the locations of CSAMT and radon gas samplings (unpublished).

3 Geophysical analytical techniques

3.1 Controlled source audio-frequency magnetotellurics

CSAMT data were collected along a west–east-trending section (5 km), which cuts across the south–north-trending faults. This section included 251 CSAMT sites at intervals of 20 m. It was surveyed with the multifunctional electric exploration system V8 (Phoenix Corporation, Canada). The V8 receivers, a 16-bit high-frequency signal, and 24-bit low-frequency signal instrument have a high dynamic range and resolution. The horizontal electric dipole transmitter was located at E 128°8′18ʺ, N 42°23′14ʺ, and the emission current was about 20 A. The distance between the transmitter and receiver was 9.5 km. The scalar mode was used for the measurements, and the frequency band was set from 1 to 9,600 Hz (Figure 4). We used a high-frequency AMTC-30 sensor and solid nonpolarized poles for collecting magnetic and electric field signals, respectively.

Figure 4 
                  An outline of the CSAMT field survey undertaken for this work. The transmitter provides the electromagnetic signal at different frequencies, and the receivers measured the electrical and magnetic signals in the far-field with the polar and magnetic probe, respectively. A and B represent the transmitter electrodes.
Figure 4

An outline of the CSAMT field survey undertaken for this work. The transmitter provides the electromagnetic signal at different frequencies, and the receivers measured the electrical and magnetic signals in the far-field with the polar and magnetic probe, respectively. A and B represent the transmitter electrodes.

Before inversion, the raw data were evaluated and preliminary data processing was carried out to ensure the data quality. First, we used a 50 Hz notch filter to remove the industrial noise. Figure 5 displays the Cagniard resistivity and impedance phase of each CSAMT site. The curves in the pseudosection map are smooth, which indicates that the raw data are reliable. Then, a low-pass Hanning window spatial filter was applied to eliminate the static effect. Finally, the resistivity inversion was carried out using the commercial software Scs2D.exe (Zonge Company, United States of America). The adoption of transverse electric (TE) and transverse magnetic (TM) modes for the inversion is more in accord with the geological model. After three iterations, the overall root mean square misfit was reduced to 1.13.

Figure 5 
                  The pseudosection of the Cagniard resistivity and impedance phase map derived from the CSAMT profile. The X-axis shows the site locations and the Y-axis shows the frequency of each site.
Figure 5

The pseudosection of the Cagniard resistivity and impedance phase map derived from the CSAMT profile. The X-axis shows the site locations and the Y-axis shows the frequency of each site.

3.2 Radon gas survey

A total of 251 samples were measured using an FD-3017 type radon gas detector (Shanghaishenke Corporation, China) [16]. The measurement accuracy of this type of active detector is 0.1 Bq/m3, with a sample spacing of 20 m along the same profile as the CSAMT survey. The measurement duration was 2 min. The workflow of the FD-3017 equipment is as follows. First, a pilot hole is drilled by the fine steel in the soil, and the radon gas sampler is inserted into the drilled hole to a depth of more than 0.7 m. Then, the air pump is lifted to the high position, and the valve is closed to isolate the gas in the cylinder desiccant from the outside. The field radon gas is then captured in the reactor. Finally, high pressure is applied for measuring the radon gas underground in the air pump (Figure 6).

Figure 6 
                  Schematic diagram of the sampling process using the active radon (222Rn) gas detector.
Figure 6

Schematic diagram of the sampling process using the active radon (222Rn) gas detector.

The water chemistry iteration method was adopted for radon gas data processing. First, we calculate the average value (X 1) and the standard deviation (SD1). Then, a new data set is obtained by eliminating those higher values that were more than three times the standard deviation from the average. The previous steps are then repeated until no higher values appear. Eventually, the final average is calculated, and the background value is set as 3,000 Bq/m3. Moreover, according to the formulas X 1 + 0.5SD1, X 1 + 2SD1, and X 1 + 3SD1, the radon data are divided into a normal zone, a medium anomalous zone, and a high anomalous zone, respectively.

4 Results

4.1 CSAMT results

The CSAMT results provided a high resolved resistivity section of the crust in Erdaobaihe (Figure 7b). From the surface to an elevation of around 600 m, the CSAMT profile shows high resistivity, which is associated with basalts. From 600 to 0 m, between sites 10 and 23, the intermediate apparent resistivity is associated with the volcanoclastic rock and quartzose sandstone (Figure 7b). Based on the regional physical properties and drilling data [17], we suggest that the deeper subsurface geological body (high resistivity) consists of marble with undeveloped fractures. At site 23, a sudden physical property change in apparent resistivity (from 3,000 to 300 Ωm) is interpreted as a brittle fracture zone (named F2), which separates quartzose sandstone from marble on the surface. At sites 23–60, the pseudo section of resistivity consists of a widespread high-resistivity anomaly intercalating with some banded low-resistivity anomalies. Regarding the high-resistivity anomaly, this corresponds to the marble which is the typical lithology of the metamorphic basement in Changbaishan. For the low-resistivity anomalies (sites 44, 46, and 48), considering their high water-bearing capacity and strong electrical conductivity, they should be regarded as marble with developed fractures. Moreover, the locations of these low-resistivity anomalies coincided with the low values of the topographic curve (Figure 7a). Combining with the fact that there are three ascending springs near site 44 (Figure 3), we argued that these low-resistivity anomalies above could therefore act as vessels for hot water to migrate from the deep thermal reservoir into shallower crust.

Figure 7 
                  (a) The topographic curve map. (b) The CSAMT nonlinear conjugate gradient inversion map. (c) The cumulative production at each site of radon (222Rn) gas. The X-axis shows the site locations.
Figure 7

(a) The topographic curve map. (b) The CSAMT nonlinear conjugate gradient inversion map. (c) The cumulative production at each site of radon (222Rn) gas. The X-axis shows the site locations.

4.2 Radon gas result

The distribution of radon gas is influenced by two factors: water temperature and fracturing. The first factor affects the volatility of radon in hot springs and the second influences the radon gas discharge [16]. Figure 7c presents the cumulative production at each site of radon (222Rn) gas. Based on the curve shape of the radon, part statistics are carried out to eliminate disturbing factors of the radon. Along the profile, the radon distribution was divided into four zones. The first and the third zones have generally low values of radon, indicating low total heat energy or undeveloped fractures. The second and the fourth zones show generally high values, indicating the available heat energy is much greater, suggesting the potential for geothermal exploitation. Moreover, the values in these zones are all significantly higher than the background level (blue horizontal line, Figure 7c). Considering the maximum and minimum values of the measured radon gas, the natural background value and the anomaly threshold should be set to 3,000 and 14,000 Bq/m3, respectively. Thus, two coniform tooth-shape anomalies are identified at sites 20 and 23 with radon gas values of 19,000 and 24,000 Bq/m3, respectively. Three bullet tooth-shape anomalies are identified at sites 44, 46, and 48 with values of 24,000, 18,000, and 14,500 Bq/m3, respectively. These results further verify the locations of fractures presented in Section 4.1.

5 Discussion

5.1 Geothermal resource potential assessment of Erdaobaihe

As a world-famous active volcanic field, there are many hot springs rising to the shallow surface in the CHGF. This fact indicates that favorable geothermal resource elements for this area are as follows: an abundant heat source, good storage performance, and high levels of fluid migration. However, due to the thick covering of Quaternary basalts in Erdaobaihe, only limited information can be obtained, which makes it difficult for geologists to assess the geothermal potential in this area.

The results from the CSAMT method indicate that the deep geological body is a marble. Abundant low- to medium-temperature hot springs in Italy have shown that marble can act as a reservoir for geothermal resources [18]. High apparent resistivity extending from shallow to greater depths suggests a thick reservoir. Five-banded low-apparent resistivity anomalies indicate that there exist water-bearing fracture zones or karst caves in the marble. Owing to its high storage and drainage capacity, marble is a good reservoir for geothermal resources, especially cracked marble. Combining these results with the regional geological map (Figure 2), these faults are the secondary faults of the Tianchi-Erdaobaihe fault. Widespread faults play an important role in geothermal water movements, such as recharge and discharge. Moreover, these faults are all located in the high-value radon gas zones, which indicate high heat energies and potential geothermal reservoirs. According to previously published data in geothermometry, Erdaobaihe is located in a relatively high-temperature belt in the geothermal anomaly distribution map, with the geothermal gradient greater than 3.0°C/100 m [19]. The deep-seated magma chamber beneath Tianchi directly serves as a heat source for the Hubin, Julong, and Jinjiang hot springs, and then by means of convention it indirectly disseminates heat towards Changbai and Erdaobaihe (Figure 8). Thus, Erdaobaihe has met the aforementioned geothermal resource conditions.

Figure 8 
                  Geological sketch showing the formation of geothermal resources in the CHGF. The inset shows an image of the study area with the Hubin, Julong, and Jinjiang hot springs marked.
Figure 8

Geological sketch showing the formation of geothermal resources in the CHGF. The inset shows an image of the study area with the Hubin, Julong, and Jinjiang hot springs marked.

5.2 Geothermal resource in Erdaobaihe

Generally, the classical genesis model of geothermal systems can be classified into two types: the uplift mountain geothermal systems (I type) and sedimentary basin geothermal systems (II type) [20]. According to the basic characteristics of geothermal systems, as described in Figure 9, Type I can be further divided into additional subtypes, magmatic (Type I-1) and nonmagmatic (Type I-2) [20]. Information on the geological setting, heat source, heat flow, water resources, lithology, porosity, fluid distribution, and thermal transfer mode is listed in Table 1 and presented in Figure 9 [21]. Based on the results of the CSAMT profiles, no trace of Cenozoic magma has been found underlying Erdaobaihe, while the lithology of the geothermal reservoir is carbonate rocks (marble). Moreover, according to the four deep seismic profiles from Tianchi to Erdaobaihe, there are two magma chambers beneath the Tianchi volcano and the Western Tianchi Protection Station, which further verifies the interpretation inferred from the CSAMT profile [22]. Five-banded low-resistance anomalies (the values <500 Ωm) extending from the subsurface to depths of 800 m below sea level were interpreted to be the fractures developing in the marble. Combined with the results of the radon gas profile, the locations of the fractures discussed above were further confirmed and the geothermal gradient in these sites should be higher than others. The annual precipitation of 1347.3 mm indicates that the water source is atmospheric precipitation [23]. The north-south trending fault could also form steaming grounds and fumaroles during the fluids’ ascent to the surface (Figure 2). In addition, considering the distribution of Hubin, Julong, Jinjiang, Changbai, and Erdaobaihe hot springs, the first three should be assigned to the I1-1 type and the latter two are of I2-1 type (Table 1).

Figure 9 
                  Diagrammatic sketch of a simplified geological cross section of the different types of geothermal systems (modified from Zhang et al. [24]).
Figure 9

Diagrammatic sketch of a simplified geological cross section of the different types of geothermal systems (modified from Zhang et al. [24]).

Table 1

Basic characteristics of the geothermal system

Classification Uplift mountain (I type)
Geological setting Plate margin and interplate uplift
Subtype Magmatic type (I1 type) Nonmagmatic type (I2 type)
Hot source Intrusion, volcano, and melts Radioactive decay, heat conduction
Heat flow (mW/m2) >85 60–100
Geothermal resource Hydrothermal type (I1-1 type) Dry-hot type (I1-2 type) Hydrothermal type (I2-1 type) Dry-hot type (I2-2 type)
Water source Mainly of atmospheric precipitation with minor magmatic water and seawater Non or only minor magmatic or sedimentary water Mainly of atmospheric precipitation with minor seawater Non or only minor sedimentary water
Lithology Volcanics, intrusion, metamorphic, and sedimentary rocks Intrusion, metamorphic, and sedimentary rocks Primarily granite; secondly volcanic, metamorphic, and sedimentary rock Primarily granite secondly volcanic, metamorphic, and sedimentary rock
Porosity Mainly of fracture with minor pores Non or only with minor fracture and pores Mainly of fracture with minor pores Non or only with minor fracture and pores
Temperature >150°C 150–300°C 40–150°C >150°C
Geothermal distribution Localization Universality Localization Universality
Geothermal gradient (°C/hm) >4 >4 >3 >3
Geothermal channel Fracture development Locally fracture development Fracture development Locally fracture development
Thermal transfer Convection Conduction Convection Conduction
Classic example Yambajam, Tibet Kraft, Iceland In this paper Zhangzhou, Fujian

6 Conclusions

  1. Based on the results of the CSAMT profile, five-banded low-resistance anomalies regarded as geothermal channels were identified.

  2. The radon gas results further confirmed these fracture locations and the geothermal gradient in these sites should be higher than others.

  3. Erdaobaihe has met the aforementioned conditions for the existence of geothermal resources that could be exploited.

  4. According to the basic characteristics of a geothermal system, Erdaobaihe geothermal resource should be assigned to the I2-1 type.

Acknowledgments

We thank the managing editor, Jan Barabach, for his efforts and patients in dealing with our manuscript. We thank the reviewers’ scientific attitude and responsible spirit, from which we have gained a great deal of new knowledge and skills. We gratefully acknowledge the support from the Jilin Provincial Geological Exploration Fund Project [grant number 2018-14].

  1. Funding information: Jilin Provincial Geological Exploration Fund Project [grant number 2018-14].

  2. Author contributions: Z.J.S. designed carried out the surveys. Z.H.X. performed the geophysical data processing. W.X. and L.Z. prepared the manuscript with contributions from all co-authors.

  3. Conflict of interest: Authors state no conflict of interest.

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Received: 2021-03-22
Revised: 2021-05-31
Accepted: 2021-07-20
Published Online: 2021-09-20

© 2021 Zhi-He Xu et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  40. Ecological risk assessment of toxic metal pollution in the industrial zone on the northern slope of the East Tianshan Mountains in Xinjiang, NW China
  41. Seasonal color matching method of ornamental plants in urban landscape construction
  42. Influence of interbedded rock association and fracture characteristics on gas accumulation in the lower Silurian Shiniulan formation, Northern Guizhou Province
  43. Spatiotemporal variation in groundwater level within the Manas River Basin, Northwest China: Relative impacts of natural and human factors
  44. GIS and geographical analysis of the main harbors in the world
  45. Laboratory test and numerical simulation of composite geomembrane leakage in plain reservoir
  46. Structural deformation characteristics of the Lower Yangtze area in South China and its structural physical simulation experiments
  47. Analysis on vegetation cover changes and the driving factors in the mid-lower reaches of Hanjiang River Basin between 2001 and 2015
  48. Extraction of road boundary from MLS data using laser scanner ground trajectory
  49. Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud
  50. Research on the conservation and sustainable development strategies of modern historical heritage in the Dabie Mountains based on GIS
  51. Cenozoic paleostress field of tectonic evolution in Qaidam Basin, northern Tibet
  52. Sedimentary facies, stratigraphy, and depositional environments of the Ecca Group, Karoo Supergroup in the Eastern Cape Province of South Africa
  53. Water deep mapping from HJ-1B satellite data by a deep network model in the sea area of Pearl River Estuary, China
  54. Identifying the density of grassland fire points with kernel density estimation based on spatial distribution characteristics
  55. A machine learning-driven stochastic simulation of underground sulfide distribution with multiple constraints
  56. Origin of the low-medium temperature hot springs around Nanjing, China
  57. LCBRG: A lane-level road cluster mining algorithm with bidirectional region growing
  58. Constructing 3D geological models based on large-scale geological maps
  59. Crops planting structure and karst rocky desertification analysis by Sentinel-1 data
  60. Physical, geochemical, and clay mineralogical properties of unstable soil slopes in the Cameron Highlands
  61. Estimation of total groundwater reserves and delineation of weathered/fault zones for aquifer potential: A case study from the Federal District of Brazil
  62. Characteristic and paleoenvironment significance of microbially induced sedimentary structures (MISS) in terrestrial facies across P-T boundary in Western Henan Province, North China
  63. Experimental study on the behavior of MSE wall having full-height rigid facing and segmental panel-type wall facing
  64. Prediction of total landslide volume in watershed scale under rainfall events using a probability model
  65. Toward rainfall prediction by machine learning in Perfume River Basin, Thua Thien Hue Province, Vietnam
  66. A PLSR model to predict soil salinity using Sentinel-2 MSI data
  67. Compressive strength and thermal properties of sand–bentonite mixture
  68. Age of the lower Cambrian Vanadium deposit, East Guizhou, South China: Evidences from age of tuff and carbon isotope analysis along the Bagong section
  69. Identification and logging evaluation of poor reservoirs in X Oilfield
  70. Geothermal resource potential assessment of Erdaobaihe, Changbaishan volcanic field: Constraints from geophysics
  71. Geochemical and petrographic characteristics of sediments along the transboundary (Kenya–Tanzania) Umba River as indicators of provenance and weathering
  72. Production of a homogeneous seismic catalog based on machine learning for northeast Egypt
  73. Analysis of transport path and source distribution of winter air pollution in Shenyang
  74. Triaxial creep tests of glacitectonically disturbed stiff clay – structural, strength, and slope stability aspects
  75. Effect of groundwater fluctuation, construction, and retaining system on slope stability of Avas Hill in Hungary
  76. Spatial modeling of ground subsidence susceptibility along Al-Shamal train pathway in Saudi Arabia
  77. Pore throat characteristics of tight reservoirs by a combined mercury method: A case study of the member 2 of Xujiahe Formation in Yingshan gasfield, North Sichuan Basin
  78. Geochemistry of the mudrocks and sandstones from the Bredasdorp Basin, offshore South Africa: Implications for tectonic provenance and paleoweathering
  79. Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping
  80. Lithology classification of volcanic rocks based on conventional logging data of machine learning: A case study of the eastern depression of Liaohe oil field
  81. Sequence stratigraphy and coal accumulation model of the Taiyuan Formation in the Tashan Mine, Datong Basin, China
  82. Influence of thick soft superficial layers of seabed on ground motion and its treatment suggestions for site response analysis
  83. Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine
  84. Research on the traditional zoning, evolution, and integrated conservation of village cultural landscapes based on “production-living-ecology spaces” – A case study of villages in Meicheng, Guangdong, China
  85. A prediction method for water enrichment in aquifer based on GIS and coupled AHP–entropy model
  86. Earthflow reactivation assessment by multichannel analysis of surface waves and electrical resistivity tomography: A case study
  87. Geologic structures associated with gold mineralization in the Kirk Range area in Southern Malawi
  88. Research on the impact of expressway on its peripheral land use in Hunan Province, China
  89. Concentrations of heavy metals in PM2.5 and health risk assessment around Chinese New Year in Dalian, China
  90. Origin of carbonate cements in deep sandstone reservoirs and its significance for hydrocarbon indication: A case of Shahejie Formation in Dongying Sag
  91. Coupling the K-nearest neighbors and locally weighted linear regression with ensemble Kalman filter for data-driven data assimilation
  92. Multihazard susceptibility assessment: A case study – Municipality of Štrpce (Southern Serbia)
  93. A full-view scenario model for urban waterlogging response in a big data environment
  94. Elemental geochemistry of the Middle Jurassic shales in the northern Qaidam Basin, northwestern China: Constraints for tectonics and paleoclimate
  95. Geometric similarity of the twin collapsed glaciers in the west Tibet
  96. Improved gas sand facies classification and enhanced reservoir description based on calibrated rock physics modelling: A case study
  97. Utilization of dolerite waste powder for improving geotechnical parameters of compacted clay soil
  98. Geochemical characterization of the source rock intervals, Beni-Suef Basin, West Nile Valley, Egypt
  99. Satellite-based evaluation of temporal change in cultivated land in Southern Punjab (Multan region) through dynamics of vegetation and land surface temperature
  100. Ground motion of the Ms7.0 Jiuzhaigou earthquake
  101. Shale types and sedimentary environments of the Upper Ordovician Wufeng Formation-Member 1 of the Lower Silurian Longmaxi Formation in western Hubei Province, China
  102. An era of Sentinels in flood management: Potential of Sentinel-1, -2, and -3 satellites for effective flood management
  103. Water quality assessment and spatial–temporal variation analysis in Erhai lake, southwest China
  104. Dynamic analysis of particulate pollution in haze in Harbin city, Northeast China
  105. Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: In a case study of the Lake Tana sub-basin in northwestern Ethiopia
  106. Performance comparison of the wavenumber and spatial domain techniques for mapping basement reliefs from gravity data
  107. Spatiotemporal evolution of ecological environment quality in arid areas based on the remote sensing ecological distance index: A case study of Yuyang district in Yulin city, China
  108. Petrogenesis and tectonic significance of the Mengjiaping beschtauite in the southern Taihang mountains
  109. Review Articles
  110. The significance of scanning electron microscopy (SEM) analysis on the microstructure of improved clay: An overview
  111. A review of some nonexplosive alternative methods to conventional rock blasting
  112. Retrieval of digital elevation models from Sentinel-1 radar data – open applications, techniques, and limitations
  113. A review of genetic classification and characteristics of soil cracks
  114. Potential CO2 forcing and Asian summer monsoon precipitation trends during the last 2,000 years
  115. Erratum
  116. Erratum to “Calibration of the depth invariant algorithm to monitor the tidal action of Rabigh City at the Red Sea Coast, Saudi Arabia”
  117. Rapid Communication
  118. Individual tree detection using UAV-lidar and UAV-SfM data: A tutorial for beginners
  119. Technical Note
  120. Construction and application of the 3D geo-hazard monitoring and early warning platform
  121. Enhancing the success of new dams implantation under semi-arid climate, based on a multicriteria analysis approach: Case of Marrakech region (Central Morocco)
  122. TRANSFORMATION OF TRADITIONAL CULTURAL LANDSCAPES - Koper 2019
  123. The “changing actor” and the transformation of landscapes
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