Home Multisource remote sensing image fusion processing in plateau seismic region feature information extraction and application analysis – An example of the Menyuan Ms6.9 earthquake on January 8, 2022
Article Open Access

Multisource remote sensing image fusion processing in plateau seismic region feature information extraction and application analysis – An example of the Menyuan Ms6.9 earthquake on January 8, 2022

  • Nana Zhang , Long Li , Jun Li , Gang Jiang , Yujun Ma and Yuejing Ge EMAIL logo
Published/Copyright: February 15, 2024
Download Article (PDF)
Become an author with De Gruyter Brill
Submit Manuscript Author Information
Open Geosciences
From the journal Open Geosciences Volume 16 Issue 1

Abstract

A 6.9 magnitude earthquake hit Menyuan County, Haibei Prefecture, Qinghai Province, at 01:45 PM Beijing time on January 8, 2022 (17:45 PM GMT time on January 7, 2022). To explore the magnitude of the earthquake deformation and the affected area, this work combined optical remote sensing interpretation, interferometric synthetic aperture radar (InSAR) coseismic deformation extraction, and field surveys for research and analysis. Relying on the high-resolution Earth observation system of the Qinghai Remote Sensing Center for Natural Resources, high-resolution GF1D, GF2, and TRIPLESAT optical remote sensing images were acquired immediately after the earthquake. The airborne triangulation encryption method was used to carry out orthographic correction, fusion, and mosaic processing of digital orthophoto map (DOM) and digital surface model (DSM) images, and first-hand optical remote sensing images of the disaster areas were obtained. Based on differential InSAR (D-InSAR), small baseline subset InSAR (SBAS-InSAR) and lifting rail fusion methods, the coseismic deformation field and deformation rate of the lifting rail direction were obtained by using Sentinel-1A data processing before and after the earthquake. Combined with optical interpretation, InSAR deformation, and field investigation, the results show that the deformation trend of the line of sight (LOS) images to the north and south of the ascending and descending orbits show an obvious opposite trend. The surface shape variables are −50 to 45 cm and −65 to 72 cm, respectively, and the deformation rate before the earthquake reached 25 cm/year. The deformation field characteristics show that the earthquake was mainly due to thrust, and the coseismic deformation field fractured along the WNW‒ESE direction with a length of approximately 33 km. The areas affected by 10 mm, 20 cm, and 50 cm deformation magnitudes in the whole earthquake area were 975.14, 321.10, and 38.55 km2, respectively. Within 20 km, there were two main affected townships, namely, Sujitan Township and Huangcheng Mongolian Township. Within 50 km, there were four main affected towns and townships, namely, Sujitan Township, Mongolian Township of the Imperial city, Qingshizui town, and Haomen town.

Keywords: DOM; DEM; D-InSAR; SBAS-InSAR; lifting rail fusion; fusion processing

1 Introduction

Earthquakes are some of the most serious natural disasters; they cause casualties, and their suddenness, instantaneity, and associated disasters have caused great harm to human society. On May 12, 2008, a Ms8.0 earthquake occurred in Wenchuan, China. The seismic waves circled the Earth six times, affecting more than half of China and multiple countries and regions in Asia, causing enormous casualties and property damage. On February 6, 2023, two earthquakes occurred in Turkey, Ms7.8 and Ms7.5, causing enormous property losses and casualties, and these earthquakes attracted the general attention of the international community. With the rapid development of the modern economy, population concentration in cities, and the increasing degree of urbanization in cities, the losses caused by earthquake disasters are intensifying. Exploring the magnitude of earthquake deformation and that in the affected area can provide a reference for rapid assessment of loss after disasters. Multisource remote sensing image fusion is beneficial for enhancing the ability of multiple data analyses and environmental dynamic detection, improving the timeliness and reliability of remote sensing information extraction, and effectively improving the use of data.

On January 8, 2022 01:45 Beijing time (UTC 2022-01-7 17:45:27), a magnitude 6.9 earthquake hit Menyuan County, Haibei Prefecture, Qinghai Province. The epicenter was located at 101.26 degrees east longitude and 37.77 degrees north latitude, with a focal depth of approximately 10 km and a rupture duration of approximately 13 s. The result of the focal mechanism solution of this earthquake indicates that the moment magnitude Mw is approximately 6.6. Fault nodal plane 1 strikes 193°, dips 89°, and has a slip angle of −159°. Fault nodal plane 2 strikes 102°, dips 69°, and has a slip angle of −2°. The depth of the center of mass of the fitted waveform is 11 km, and it is preliminarily inferred that this earthquake was a main strike-slip event [1,2,3,4]. According to historical earthquake statistics since 1970, 54 earthquakes of magnitude M3.0 and greater have occurred within 50 km of the region. A total of 170 earthquakes of magnitude M3.0 and greater have occurred within 100 km of the area; the earthquake that occurred on January 8, 2022, was the largest earthquake in history [5,6,7]. After the earthquake, we arrived at the Mongolian township of Huangcheng, which is the closest township to the epicenter, and carried out investigations on earthquake disasters, landslides, and personnel losses.

The Lenglongling fault is located in the central and western segments of the Qilian–Haiyuan fault zone and is an important part of the Tianzhu seismic gap [8,9]. The west end of the fault and the Tolaishan fault are distributed in a series of left-lateral oblique faults, the east end is divided into the Gulang fault and the Jinqianghe fault, and to the east, the west end of the fault and the Tolaishan fault are connected to the Tianjingshan fault zone of Xiangshan and the Maomaoshan–Louhushan–Haiyuan fault zone, respectively [10,11]. The fracture is approximately 120 km long, with obvious linear features. This area is one of the regions with the strongest tectonic and seismic activity in China [12,13,14]. In studying the Menyuan earthquake, many scholars have carried out long-term research. Researchers have generally concluded that a series of large-scale left-lateral dislocation landforms are distributed along the Lenglongling fault, and the fault has left-lateral strike-slip movement [15,16,17]. Since the Holocene, the Lenglongling fault zone has been mainly characterized by left-lateral strike-slip movement with local dip-slip components. Zheng et al. [18] determined that the Lenglongling fault is an important left-lateral strike-slip fault on the northeastern margin of the Qinghai‒Tibet Plateau, and together with the Tolaishan fault, the Jinqianghe fault, the Maomaoshan fault, the Laohushan fault, and the Haiyuan fault, the Lenglongling fault constitutes the Qilian–Haiyuan fault zone. Gaudemer et al. [15] reported that the slip rate of the Lenglongling fault was 15 ± 5 mm/a by means of SPOT satellite image interpretation. Lasserre et al. [17] found that the slip rate of the Lenglongling fault was 19 ± 5 mm/a through field investigation.

The Holocene slip rate obtained by Zheng et al. [18] after studying the river terraces was 4.4 ± 0.7 mm/a. He et al. [16] judged that the Holocene slip rate of the fault was 3.9 ± 0.36 mm/a and believed that the fault zone was mainly characterized by left-lateral strike-slip movement in the late Quaternary period (2010). The fault zone was analyzed via the GPS crustal motion velocity field, and the results revealed that the direction of the GPS velocity vector of the northeastern Qinghai‒Tibet Plateau from west to east gradually changed from NE to ENE and finally to ESE [19,20,21].

Interferometric synthetic aperture radar (InSAR) can obtain large-scale and high-resolution surface deformation information [22,23,24], which can provide a good constraint on the depth of shallow earthquakes. Inversion combined with geodetic data helps to better understand the distribution of seismic slip and fault tectonic characteristics [25]. The early activity of the fault was dominated by compression and thrust. Since approximately the middle Pleistocene, the fault activity has been dominated by left-lateral strike-slip, with a normal fault component [16]. During the late Quaternary period, the fault activity was intense, mainly manifested as left-lateral strike-slip movement, and a series of significant fault-displacement landforms formed along the fault. There are many synchronous left-handed dislocations, such as those of water systems, alluvial terraces, glacial valleys, glacial carbon ridges, and mountain ridges [26,27,28].

To explore the magnitude of the earthquake deformation and the affected area, this work combined optical remote sensing interpretation, InSAR coseismic deformation extraction, and field surveys for research and analysis. Relying on the high-resolution Earth observation system of the Qinghai Remote Sensing Center for Natural Resources, high-resolution GF1D, GF2, and TRIPLESAT optical remote sensing images were acquired immediately after the earthquake. The airborne triangulation encryption method was used to carry out orthographic correction, fusion and mosaic processing of digital orthophoto map (DOM) and digital surface model (DSM) images, and first-hand optical remote sensing images of the disaster areas were obtained. Based on differential InSAR (D-InSAR), small baseline subset InSAR (SBAS-InSAR), and lifting rail fusion methods, the coseismic deformation field and deformation rate of the lifting rail direction were obtained by using Sentinel-1A data processing before and after the earthquake. The specific research area is shown in Figure 1.

Figure 1 Regional distribution of researched events.
Figure 1

Regional distribution of researched events.

2 Methods

2.1 Data processing model

2.1.1 D-InSAR model

The method used in this work is the two-track differential interference processing method [28]. The basic idea of the two-track method is to use the two images before and after the surface change in the study area to generate an interference fringe map, use the baseline information and the external reference digital elevation model (DEM) to simulate the terrain phase of the study area, and subtract the terrain information from the interference fringe map to obtain the surface change information. The interferometric phase composition can be expressed as follows:

(1) φ = φ flat + φ top + φ def + φ atm + φ noise ,

where φ is the interference phase obtained from the image; φ flat is the flat ground effect phase, which can be removed by precise orbit data; φ top is the terrain phase, which can be eliminated by using an external DEM to simulate the terrain phase; φ atm is the atmospheric delay phase; φ noise is the noise phase, which can be removed by filter attenuation or removal; and φ def is the surface deformation phase.

2.1.2 SBAS-InSAR model

SBAS-InSAR uses short baseline differential interferometry [29,30]. According to the relationship between the coherent pixel phase and the observation time, the singular value decomposition method is used to jointly solve the data of multiple small baseline sets, which effectively solves the time discontinuity problem caused by the long spatial baseline between each dataset. The temporal resolution of monitoring is improved so that the minimum norm least squares solution of the surface deformation rate between image sequences can be obtained. The basic principle is to assume that there are N + 1 single-view complex images, the imaging time is ( t 0 , t 1 , … , t n ) , and a differential unwrapped image is j. The unwrapped phase corresponding to a pixel can be expressed as formula (2).

(2) δ φ j ( x , r ) = φ ( t B , x , r ) − φ ( t A , x , r ) ≈ δ φ J TOPO ( x , r ) + δ φ j disp ( x , r ) + δ φ j atm ( x , r ) + δ φ j noise ( x , r ) ,

(3) δ φ J TOPO ( x , r ) = 4 π τ B ⊥ j ∆ Z R sin θ δ φ j disp ( x , r ) = 4 π τ [ d ( t B , x , r ) − d ( t A , x , r ) ] δ φ j atm ( x , r ) = φ atm ( t B , x , r ) − φ atm ( t A , x , r ) ,

where δ φ J TOPO ( x , r ) is the phase formed by the DEM error, δ φ j disp ( x , r ) is the phase caused by the deformation of the slope, δ φ j atm ( x , r ) is the phase caused by atmospheric influence between time t A and t B , δ φ j atm ( x , r ) is the phase caused by noise, τ is the wavelength, θ is the radar angle of view, ∆ Z is the DEM error, R is the slant distance between the radar and the observed object, and d ( t B , x , r ) and d ( t A , x , r ) represent the cumulative amount of deformation in the viewing direction with respect to the reference time t o . M is the number of interferograms.

Assuming that N represents the total number of SAR images, there are M 2 ≤ M ≤ N ( N − 1 ) 2 to perform least multiplication or singular value decomposition on M unwrapped phases. Since M interferograms are generated during processing, M equations can be obtained according to formula (3), which are expressed as formula (4) in matrix form.

(4) δ φ j ( x , r ) = A φ ( x , r ) ,

where A is the MXN coefficient matrix and φ ( x , r ) is the matrix formed by the unknown deformation phase corresponding to point ( x , r ) at N times. When N ≤ M , formula (5) is obtained by the least squares method.

(5) φ ( x , r ) = ( A T A ) − 1 A T δ φ ( x , r ) .

When M < N, the equation has countless solutions, and the SVD method is used to jointly solve several small baselines. Finally, the cumulative deformation corresponding to different times can be obtained.

2.1.3 2D deformation transformation

Since landslides mostly slide along the slope, the deformation information in the direction of the radar line of sight (LOS) cannot accurately reflect the real deformation of the slope [31]. Considering the geometric relationship between the radar sight direction, the slope direction, and the vertical subsidence direction, assuming that the motion occurs in the direction specified by the unit vector u, using formulas (5), (6), and (8), the deformation rate of the LOS direction is converted into the deformation rate of the slope direction and the vertical direction [32,33].

(6) V u = V LOS + V Slope sin θ cos δ − α s − 3 2 π cos θ ,

(7) V Slope = V LOS / cos β ,

(8) cos β = ( − sin α cos φ ) ( − sin θ cos α s ) ( − cos α cos φ ) ( − sin θ sin α s ) sin φ cos θ ,

where V Slope represents the deformation rate along the slope direction, V LOS represents the deformation rate along the radar line of sight, and V u represents the deformation rate in the vertical direction. α s is the angle between the azimuth and true north, and α s − 3 2 π is the azimuth to the line of sight. δ is the azimuth angle of the landslide, α is the slope aspect, β is the apparent slope angle, θ is the incident angle, and φ is the slope gradient.

2.2 Technical monitoring methods

2.2.1 InSAR monitoring technology and method

InSAR can obtain large-scale and high-resolution surface deformation information, which can provide good constraints on the depth of shallow earthquakes. Inversion combined with geodetic data helps to better understand the distribution of seismic slip and fault tectonic characteristics [23,25]. The interferometric wide mode of Sentinel-1A uses medium resolution (20 m) to obtain images with a width of 250 km. It acquires three sub-bands by adopting a progressive terrain scanning method, and the data adopt the C-band with a wavelength of 5 cm, which is very sensitive to surface deformation. The vertical spatial baselines of the master and slave image pairs of SAR data are both <15 m, far below the critical baseline value of more than 1,000 m, and the time interval is within 1 month. The selection of the ideal spatiotemporal baseline can effectively reduce the influence of spatiotemporal decorrelation on the obtained deformation field results. In this monitoring, the D-InSAR method and SBAS-InSAR method were used to obtain 40 scenes of Sentinel-1A radar satellite images. InSAR deformation monitoring of the Mw6.9 earthquake in Menyuan County, Haibei Prefecture, was carried out to identify the displacement and deformation of the Lenglongling fault zone and the surrounding features of Menyuan County before the earthquake.

D-InSAR differential interferometry processing steps include image coarse registration, image cropping, image fine registration, small baseline interferometric image pair combination, interferogram generation, simulated terrain phase, de-terrain phase, phase filtering, etc.

The SBAS-InSAR (small baselines) short baseline set connects the independent SAR images caused by the long baseline to form a short baseline SAR image set to increase the sampling rate of data acquisition so that several small sets can be formed in the existing SAR image dataset. The baseline between SAR images within each small ensemble is smaller, and the baseline between SAR images between ensembles is larger.

2.2.2 Optical image monitoring technology method

Relying on the high-resolution Earth Observation System of Qinghai Provincial Natural Resources Remote Sensing Center for the first postearthquake data, the Qinghai Data and Application Center (Gaofen Qinghai Center) Multisource Remote Sensing Data Instant Service System collected 65 original satellite images of GF1D, GF2, and TRIPLESAT from January to August 2022, including panchromatic and multispectral images of co-orbit and simultaneous phases, all with high-precision satellite ephemeris and attitude data files.

Data processing includes aerial triangulation encryption (data preparation, relative orientation and model connection, and aerial triangulation adjustment); satellite image DEM production (orientation modeling, epipolar resampling, feature point, line measurement, image DEM and editing, object DEM editing, DEM edge, and DEM map mosaic and cropping); and satellite image DOM production (external parameter calculation, panchromatic band image orthorectification, multispectral image orthorectification, image registration correction, image fusion processing, image enhancement processing, and mosaic and cropping). The specific technical route for this experiment is shown in Figure 2.

Figure 2 Flow chart of InSAR overall monitoring technology.
Figure 2

Flow chart of InSAR overall monitoring technology.

3 Results

3.1 SBAS-InSAR data processing results before the earthquake

A total of 38 scenes from August 19, 2020 to January 5, 2022 in the C-band (5.6 cm), with a resolution of 5 m × 20 m, and Sentinel-1A images in VV polarization mode were selected as the data source for extracting the deformation rate of the region in the LOS direction. Sentinel-1A has an average central incidence angle of 43.88° and adopts the interferometric wide swath (IW) imaging mode. The DEM is the Shuttle Radar Topography Mission (SRTM) DEM with an accuracy of 30 m provided by NASA to deduct the terrain phase in the SAR interferogram.

The spatial baseline map shows that each SAR image scene is well combined with adjacent SAR images for interferometric image pairing (Figures 3 and 4). The maximum spatial baseline distance is 150 m, the minimum spatial baseline distance is −100 m, and the remaining spatial baselines are approximately ±50 m. This shows that the coherence of the original SAR image is relatively high, and the spatial baseline is relatively reliable. The distribution period of the temporal baseline is also relatively regular, and the baseline distance is balanced. Combining the spatial baseline and the temporal baseline, the selected 38-scene SAR images are reasonable in both space and time, providing reliable data support for subsequent high-precision deformation rate extraction.

Figure 3 Spatial baseline map.
Figure 3

Spatial baseline map.

Figure 4 Temporal baseline diagram.
Figure 4

Temporal baseline diagram.

Due to the extremely unstable geological tectonic activity in the study area, linear and nonlinear deformation information may exist in the deformation recorded in the survey area. The SBAS-InSAR method is used to extract the deformation and deformation rate information in this area. Among them, the interferogram is a multiview interferogram generated from the intensity map of the registered master-slave image pairs. The flattened interferogram is a multiview flattened interferogram generated by registering the intensity map of the master-slave image pair, synthesized phase, and diagonal DEM. Afterward, phase unwrapping is performed on the smoothed and filtered phase to solve the problem of 2π ambiguity. Accounting for the influence of the spatiotemporal baseline and Doppler effect during the registration process, the August 2, 2021 daily image was selected as the public main image of all images. The other images are registered with the main image, and each phase is connected with all other phases, ensuring that each image is evenly connected to at least five other images. Differential interference is performed on the image, and the interference that exceeds the threshold of the spatiotemporal baseline is eliminated to avoid spatial decoherence caused by a spatial baseline and temporal decoherence that is overly short caused by a temporal baseline that is overly long. Finally, 425 pairs of interference image pairs that meet the threshold condition of the spatiotemporal baseline are obtained, as shown in Figures 5–7.

Figure 5 20200912–20200831 image pair diagram. (a) Coherence factor diagram. (b) Interferogram after filtering. (c) Phase-disentanglement diagram. (d) Interferogram after de-leveling.
Figure 5

20200912–20200831 image pair diagram. (a) Coherence factor diagram. (b) Interferogram after filtering. (c) Phase-disentanglement diagram. (d) Interferogram after de-leveling.

Figure 6 20200912–20201123 image pair diagram. (a) Coherence factor diagram. (b) Interferogram after filtering. (c) Phase-disentanglement diagram. (d) Interferogram after de-leveling.
Figure 6

20200912–20201123 image pair diagram. (a) Coherence factor diagram. (b) Interferogram after filtering. (c) Phase-disentanglement diagram. (d) Interferogram after de-leveling.

Figure 7 20210721–20210615 image pair diagram. (a) Coherence factor diagram. (b) Interferogram after filtering. (c) Phase-disentanglement diagram. (d) Interferogram after de-leveling.
Figure 7

20210721–20210615 image pair diagram. (a) Coherence factor diagram. (b) Interferogram after filtering. (c) Phase-disentanglement diagram. (d) Interferogram after de-leveling.

After the SBAS-InSAR processing is completed, the time series deformation map of the Menyuan County Mw6.9 earthquake before the earthquake is obtained, as shown in Figure 8. The obvious displacement and deformation that occurred in the entire monitoring area from August 19, 2020 to January 5, 2022 were extracted, and the central area (Lenglongling fault zone) was the most sensitive to displacement and deformation. Figure 8 shows that the average annual deformation rate reaches 220 mm/year, and the average annual deformation rate in the southern region (Menyuan County) is approximately 8–50 mm/year. The average annual deformation rate in the northern region is approximately 20–80 mm/year. The area with the largest amount of deformation runs through the Lenglongling fault zone from west to east and from south to north, and the areas with the largest displacement change are also in this fault zone, which is related to the more active geological tectonic activities in the Lenglongling fault zone. The deformation variables on the north and south sides are smaller than those in the central region. Preliminary estimates show that the regional average displacement and deformation are changing year by year, which provides a priori conditions for the occurrence of earthquakes.

Figure 8 Pre-earthquake time series deformation diagram of the Mw6.9 magnitude earthquake in Menyuan County, Haibei Prefecture, Qinghai Province.
Figure 8

Pre-earthquake time series deformation diagram of the Mw6.9 magnitude earthquake in Menyuan County, Haibei Prefecture, Qinghai Province.

Combined with the above situation, the fault zone is affected by the northward pushing of the Qinghai‒Tibet block, and the Qilian Mountains are blocked by the northern Alxa block during the overall subduction in the northeast direction. At the same time, the Longshoushan uplift at the front edge of the Alxa block also begins to overthrow in the southwest direction, thus forming the Hexi Corridor Basin by subsidence and hedging. Their strikes are basically the same, with a WNW trend, and most of the developed faults have the characteristics of compression and thrusting and strike-slip motion.

3.2 D-InSAR data acquisition

Due to D-InSAR data processing, two images before and after the earthquake are needed for differential interference processing. The Sentinel-1A image data (wavelength of 5.6 cm) before and after the Menyuan earthquake area on January 8, 2022 are obtained. Among them, there are two scenes of orbit-ascending data (one scene before the earthquake and one scene after the earthquake) and two scenes of orbit-descending data (one scene before the earthquake and one scene after the earthquake). The details are shown in Table 1.

Table 1

SAR images used for D-InSAR

Earthquake time Flight direction Main image (time) Auxiliary images (time) Spatial baseline (m) Temporal baseline (days)
2022 Elevated rails 20220105 20220117 48.3 12
2022 Lowering track 20211229 20220110 55.1 12

3.3 SBAS-InSAR data acquisition

SBAS-InSAR data processing is different from D-InSAR data processing, which extracts and analyzes deformation information for the entire observation period based on long-term sequences. The Sentinel-1A (ascending orbit) data from June 3, 2021 to January 5, 2022 before the earthquake in the Menyuan earthquake area on January 8, 2022 were selected for time series analysis, and a total of 38 images in the analysis area were obtained. The specific image conditions are shown in Table 2.

Table 2

SAR images used for SBAS-InSAR

Image serial number Collection of images (time) Flight direction Temporal baseline (days) Image serial number Collection of images (time) Flight direction Temporal baseline (days)
1 20200819 Elevated rails — 20 20210603 Elevated rails 12
2 20200831 Elevated rails 12 21 20210615 Elevated rails 12
3 20200912 Elevated rails 12 22 20210627 Elevated rails 12
4 20201006 Elevated rails 24 23 20210709 Elevated rails 12
5 20201030 Elevated rails 24 24 20210721 Elevated rails 12
6 20201123 Elevated rails 24 25 20210802 Elevated rails 12
7 20201205 Elevated rails 24 26 20210814 Elevated rails 12
8 20201229 Elevated rails 24 27 20210826 Elevated rails 12
9 20210110 Elevated rails 12 28 20210907 Elevated rails 12
10 20210122 Elevated rails 12 29 20210919 Elevated rails 12
11 20210203 Elevated rails 12 30 20211001 Elevated rails 12
12 20210215 Elevated rails 12 31 20211013 Elevated rails 12
13 20210227 Elevated rails 12 32 20211025 Elevated rails 12
14 20210311 Elevated rails 12 33 20211106 Elevated rails 12
15 20210323 Elevated rails 12 34 20211018 Elevated rails 12
16 20210404 Elevated rails 12 35 20211030 Elevated rails 12
17 20210416 Elevated rails 12 36 20211212 Elevated rails 12
18 20210428 Elevated rails 12 37 20211224 Elevated rails 12
19 20210522 Elevated rails 24 38 20220105 Elevated rails 12

3.4 D-InSAR data processing and analysis of the results

Using the synthetic aperture radar image data of the Sentinel-1A satellite ascending and descending orbits before and after the Qinghai Menyuan Mw6.9 earthquake, the differential interferometry technique was used to process and analyze the data to obtain the coseismic deformation field. Sentinel-1A SAR image processing uses the InSAR technology of the two-track method to obtain the seismic coseismic surface deformation field. In the process of interferometric processing, the 30 m spatial resolution SRTM released by NASA is used to eliminate the terrain phase and perform geocoding.

To improve the signal-to-noise ratio, 3:1 (range direction: azimuth direction) multiview and filtering processing is adopted, and then the minimum cost flow algorithm is used to perform phase unwrapping. Then, the surface deformation variables of the LOS are obtained [34]. The general satellite radar atmospheric correction system is used to reduce the influence of atmospheric noise on the accuracy of the deformation field, and finally, the coseismic surface deformation field of the Menyuan earthquake in 2022 is obtained for the ascending and descending orbits.

3.4.1 Analysis of D-InSAR data processing results for orbit ascension

Ascending orbit data processing uses the D-InSAR two-orbital method. Terrain removal adopts the SRTM DEM released by NASA, and neither the SAR interferogram nor the DEM simulation interferogram removes the flat ground effect. When performing the difference, the terrain effect and the ground effect are simultaneously removed, using the precise orbit data and eliminating the orbit error based on the interference fringe frequency analysis method. The terrain elevation in the Lenglongling area changes significantly, and there are atmospheric components that are obviously related to the terrain in the areas with large differences in elevation fluctuations. The atmospheric influence is corrected by using a function model of the linear first-order approximate relationship between atmospheric path delay variation and elevation. The interferogram and coherence coefficient map after orbit and atmospheric correction are shown in Figure 9.

Figure 9 Intermediate results of the two-track method of D-InSAR processing for elevated tracks. (a) Interferogram after filtering. (b) Interferogram after deplatforming. (c) Coherence factor diagram. (d) Phase-disentanglement diagram.
Figure 9

Intermediate results of the two-track method of D-InSAR processing for elevated tracks. (a) Interferogram after filtering. (b) Interferogram after deplatforming. (c) Coherence factor diagram. (d) Phase-disentanglement diagram.

The interferometric image pair data acquisition time interval is short, and the coherence of the image is well maintained. Generally, the coseismic deformation field of the ascending orbit remains highly consistent, showing periodic fringes distributed in an elliptical state [35]. The coherence coefficient diagram shows that the coherence coefficient values in this region are between 0 and 1, indicating that the coherence is relatively reliable. Combining the filtered interferogram, the flattened interferogram, and the phase unwrapping diagram, the seismic data of this ascending orbit have obvious elliptical fringes with a butterfly effect, which have high coherence and good interference patterns. At the same time, the periodic deformation fringes are relatively regular, and they progressively advance sequentially from the outside to the inside, showing an approximate degree of deformation. The differential deformation of the north and south walls of the fault zone is more obvious [36] and the interference fringes of both the north and south walls are smooth and clear. The long axis of the deformation field ellipse is 25 km in the NW direction, and the short axis is 20 km in the NE direction, which can approximately describe the length and width of the fault zone.

The obtained ascending orbit coseismic deformation field in the seismic region (Figure 9) shows that the ascending orbit coseismic deformation field remains highly consistent, showing periodic fringes distributed in an elliptical state. In addition, the upper and lower plates of the coseismic deformation field show opposite deformation trends, and the north plate shows an obvious uplift trend. The maximum uplift along the LOS direction to the epicenter is 50 cm, the deformation within 20 km of the epicenter is more than 25 cm, and the deformation within 50 km of the epicenter is more than 8 mm. There is an obvious subsidence trend along the LOS to the south. The maximum subsidence at the epicenter is 55 cm, the deformation within 20 km of the epicenter is more than 24 cm, and the deformation within 50 km of the epicenter is more than 7 mm. This shows that the direction of this earthquake is basically parallel to the Lenglongling fault, and the surface deformation caused by it is mainly due to horizontal movement, which is consistent with the movement characteristics of strike-slip fault type earthquakes.

3.4.2 Analysis of the descending orbit data processing results

The descending orbit data processing is similar to the ascending orbit data processing, using the D-InSAR two-orbital method. Terrain removal adopts the SRTM DEM released by NASA, and neither the SAR interferogram nor the DEM simulation interferogram removes the flat ground effect during processing. During the differential process, the terrain effect and the ground effect are simultaneously removed. Precise orbital data are used, and the orbital error is eliminated based on the method of interference fringe frequency analysis. The terrain elevation in the Lenglongling area changes significantly, and there are atmospheric components that are obviously related to the terrain in the areas with large elevation fluctuations. The atmospheric influence is corrected by using a function model of the linear first-order approximation relationship between atmospheric path delay variation and elevation. The interferogram and coherence coefficient map after orbit and atmospheric correction are shown in Figure 10.

Figure 10 Intermediate results of the reduced-track D-InSAR two-track method. (a) Interferogram after filtering. (b) Interferogram after deplatforming. (c) Coherence factor diagram. (d) Phase-disentanglement diagram.
Figure 10

Intermediate results of the reduced-track D-InSAR two-track method. (a) Interferogram after filtering. (b) Interferogram after deplatforming. (c) Coherence factor diagram. (d) Phase-disentanglement diagram.

The interferometric image pair data acquisition time interval is short, and the image coherence is well maintained. Generally, the coseismic deformation field of the descending orbit remains highly consistent, showing periodic fringes in an elliptical state [35]. The coherence coefficient diagram shows that the coherence coefficient values in this region are between 0 and 1, indicating that the coherence is relatively reliable. Combining the filtered interferogram, the flattened interferogram, and the phase unwrapping diagram, the seismic data of this ascending orbit have obvious elliptical fringes with a butterfly effect, which have high coherence and good interference patterns. Moreover, the periodic deformation stripes are relatively regular and progressively advance sequentially from the outside to the inside, showing a rough degree of deformation. The differential deformation of the north and south walls of the fault zone is obvious [36]. The interference fringes of the both the north and south walls are smooth and clear. The long axis of the deformation field ellipse is 28 km in the NW direction, and the short axis is 24 km in the NE direction, which can roughly describe the length and width of the fault zone.

As shown in Figure 11, the obtained coseismic deformation field of the descending orbit in the earthquake area shows that the coseismic deformation field of the descending orbit remains highly consistent, it shows periodic fringes distributed in an elliptical state, and the upper and lower plates of the coseismic deformation field show opposite deformation states. There is a clear subsidence trend in the north plate. The maximum subsidence along the LOS direction is 69 cm, the deformation within 20 km of the epicenter is more than 30 cm, and the deformation within 50 km of the epicenter is more than 8 mm. There is an obvious uplift trend along the LOS to the south. The maximum uplift at the epicenter is 71 cm, the deformation within 20 km of the epicenter is more than 28 cm, and the deformation within 50 km of the epicenter is more than 9 mm. The results show that the direction of this earthquake is basically parallel to the Lenglongling fault, and the surface deformation that is caused is mainly due to horizontal movement, which is consistent with the movement characteristics of strike-slip fault-type earthquakes [37].

Figure 11 (a) Ascending track isoseismic deformation diagram. (b) Descending isoseismic deformation diagram.
Figure 11

(a) Ascending track isoseismic deformation diagram. (b) Descending isoseismic deformation diagram.

3.5 GF1D, GF2, and TRIPLESAT satellite image processing results

For the initial data after the earthquake, relying on the Gaofen Qinghai Center-Multisource Remote Sensing Data Real-Time Service System, the original satellite images collected from January to August 2022 were processed according to the technical methods in Section 2.2. The accuracy index of the encrypted data processing for the control point aerial triangulation and orthophoto image quality inspection index are shown in Tables 3 and 4.

Table 3

Encryption point accuracy of control point aerial triangulation

Roll call δx δy δz Roll call δx δy δz Roll call δx δy δz
DC_092 1.13 −0.81 −0.79 DC_114 0.95 −0.8 −0.52 DC_127 0.7 −0.39 0.58
DC_049 1.12 1.02 0.46 DC_187 0.95 −0.62 0.72 DC_153 0.69 −0.2 0.66
DC_064 1.11 −1.09 0.17 DC_154 0.94 −0.88 0.33 DC_100 0.68 −0.55 −0.4
DC_137 1.1 −1.08 0.2 DC_163 0.94 −0.51 −0.79 DC_042 0.65 −0.47 −0.45
DC_152 1.09 −0.47 −0.98 DC_111 0.91 −0.02 −0.91 DC_102 0.61 −0.51 0.33
DC_170 1.08 −0.47 0.97 DC_115 0.87 0.14 0.86 DC_098 0.57 −0.53 0.22
DC_054 1.07 −1.06 0.17 DC_037 0.87 0.66 −0.57 DC_056 0.43 0.08 0.43
DC_058 1.07 −0.19 1.05 DC_088 0.87 −0.8 0.35 DC_117 0.41 −0.37 0.2
DC_077 1.05 −0.96 0.44 DC_133 0.84 0.61 −0.57 DC_169 0.39 0.27 0.28
DC_062 1.03 −0.93 −0.43 DC_141 0.82 0.35 0.74 DC_143 0.37 −0.34 −0.14
DC_061 1.02 −0.4 −0.94 DC_040 0.79 −0.55 −0.56 DC_144 0.37 −0.02 0.37
DC_142 1.02 0.58 0.84 DC_035 0.79 0.53 0.59 DC_082 0.34 0.03 −0.34
DC_124 0.97 −0.08 −0.96 DC_101 0.77 −0.59 0.5 DC_065 0.26 −0.21 0.15
DC_029 0.96 0.87 −0.4 DC_195 0.73 −0.5 −0.53 DC_080 0.25 0.18 −0.18
DC_051 0.95 0.01 −0.95 DC_036 0.7 0.15 −0.69 DC_172 0.16 −0.03 0.15
Table 4

Quality control accuracy of orthophoto field control points

Name X (pixel) Y (pixel) Z (pixel) Name X (pixel) Y (pixel) Z (pixel) Name X (pixel) Y (pixel) Z (pixel)
GCP1 1.28 1.12 1.70 GCP18 0.23 −0.56 0.60 GCP35 0.58 −0.23 0.62
GCP2 1.13 −0.85 1.41 GCP19 −0.65 −0.41 0.77 GCP36 −0.07 0.43 0.44
GCP3 −0.15 −0.73 0.74 GCP20 −1.04 −1.05 1.48 GCP37 1.75 −1.99 2.65
GCP4 0.71 1.35 1.52 GCP21 0.67 −2.45 2.54 GCP38 −0.58 0.38 0.69
GCP5 −1.05 −0.12 1.05 GCP22 −0.02 −0.17 0.18 GCP39 −3.05 2.64 4.03
GCP6 −2.17 −0.82 2.32 GCP23 0.27 −0.08 0.28 GCP40 −0.09 −2.61 2.61
GCP7 −1.44 0.84 1.67 GCP24 0.22 2.73 2.74 GCP41 −0.77 3.89 3.96
GCP8 1.52 0.54 1.61 GCP25 −2.65 1.23 2.92 GCP42 −2.35 2.10 3.15
GCP9 −0.39 −1.68 1.72 GCP26 2.13 −1.05 2.37 GCP43 6.90 −3.76 7.86
GCP10 −1.82 −0.04 1.82 GCP27 0.52 −0.18 0.55 GCP44 2.33 −0.81 2.46
GCP11 0.27 −1.69 1.71 GCP28 3.18 −1.42 3.48 GCP45 −3.27 0.44 3.30
GCP12 2.12 2.07 2.96 GCP29 −3.17 1.42 3.48 GCP46 −0.81 −0.71 1.07
GCP13 0.00 0.00 0.00 GCP30 1.67 −2.57 3.06 GCP47 −1.17 −0.10 1.17
GCP14 0.01 −0.24 0.24 GCP31 −3.82 −2.43 4.53 GCP48 1.05 0.27 1.08
GCP15 −0.02 0.49 0.49 GCP32 0.00 0.00 0.00 GCP49 0.88 −0.61 1.07
GCP16 0.01 −0.24 0.24 GCP33 0.58 −0.23 0.62 GCP50 −0.11 0.64 0.65
GCP17 0.33 2.00 2.03 GCP34 −1.16 0.47 1.25 GCP51 −1.41 0.24 1.43

3.6 Fusion analysis of optical images and D-InSAR ascending orbit

To better determine the deformation magnitude of the Menyuan earthquake and the scope of the affected area, this work combines the conclusions of optical remote sensing images, InSAR coseismic deformation extraction, and field surveys to conduct research and analysis. To obtain the exact deformation rate, the ascending and descending orbital results were fused, and the fusion results of the ascending and descending orbits were then fused with the optical remote sensing interpretation image and analyzed. The fusion results are shown in Figure 12.

Figure 12 Regional maps of the main affected townships within 20 km and 50 km after the earthquake and postearthquake image maps. (a) Displays the observational imagery captured after the earthquake. (b) and (c) Present the on-site regional mappings of the townships significantly affected by the seismic event. (d) Reveals the regional delineation of the main affected townships within a post-earthquake 20 km radius. (e) Visualizes the regional layout of the principal impacted townships within a 50 km radius following the earthquake.
Figure 12

Regional maps of the main affected townships within 20 km and 50 km after the earthquake and postearthquake image maps. (a) Displays the observational imagery captured after the earthquake. (b) and (c) Present the on-site regional mappings of the townships significantly affected by the seismic event. (d) Reveals the regional delineation of the main affected townships within a post-earthquake 20 km radius. (e) Visualizes the regional layout of the principal impacted townships within a 50 km radius following the earthquake.

Clearly, the upper and lower plates of the coseismic deformation field in the lifting rail method show opposite deformation trends. At the same time, the upper and lower sides of the deformation field of the same orbit show opposite motion states, and the coseismic LOS deformation field near the fault is more reliable. The main reason is that the seismogenic fault ruptures to the surface, the deformation gradient is large, and a continuous change in the deformation phase is caused. The uplift area near the epicenter is greatly deformed, and the maximum displacement along the LOS of the descending orbit and ascending orbit moves toward or away from the satellite. Combined with the deformation pattern of the ascending orbit, it can be inferred that the epicenter of the earthquake is located approximately at the center of the uplift. This phenomenon shows that the surface deformation caused by this earthquake is mainly due to horizontal movement, which is consistent with the movement characteristics of strike-slip fault-type earthquakes [38,39]. In addition, due to the inconsistency in the LOS directions of the ascending and descending orbit radar satellites, the actual crustal deformation components along the two LOS directions are inconsistent, resulting in different coseismic deformation field distributions corresponding to the ascending and descending orbit radar images. This is mainly manifested in the difference in two aspects: the deformation variable value and the distribution range of the deformation field. In the coseismic deformation field of the ascending orbit, the maximum uplift of the surface along the LOS direction also reaches 6.0 cm, and the deformation distribution area is basically the same as that of the descending orbit [40]. The long axis is approximately N50°E, approximately 22 km long, and the short axis is approximately 17 km long. The largest uplift areas in the two coseismic deformation maps are distributed between the main fault and the branch fault in the Lenglongling fault zone, which is closer to the branch fault. Through the analysis of the surface deformation profile curve, the entire surface deformation is basically manifested as crustal uplift. It is speculated that this is because the energy released by this earthquake was low and the surface was not staggered. Therefore, the overall uplift trend is shown in the InSAR coseismic deformation map, so it is impossible to determine the exact location of the exposed surface of the fault in the map showing the coseismic deformation distribution.

Combined with optical images, postearthquake GF1D satellite orthophoto images were used to determine the maps of the affected areas in major towns and towns 20 and 50 km away from the earthquake. Among them, there are two main disaster-stricken townships within 20 km, namely, Sujitan Township and Huangcheng Mongolian Township, with an affected area of approximately 520 km2. There are four main disaster-stricken townships within 50 km, namely, Sujitan Township, Huangcheng Mongolian Township, Qingshizui town and Haomen town, with an affected area of approximately 1,333 km2.

4 Discussion

This study combines optical remote sensing interpretation, InSAR coseismic deformation extraction, and field surveys for research and analysis. Relying on the high-resolution earth observation system of the Qinghai Provincial Natural Resources Remote Sensing Center, postearthquake high-resolution GF1D, GF2, and TRIPLESAT optical remote sensing images were obtained immediately. The aerial triangulation encryption method is used to perform orthorectification, fusion, and mosaic processing of DOM and DSM, and the first-hand optical remote sensing image of the disaster area is obtained. Using D-InSAR, SBAS-InSAR, and the lifting rail fusion method, Sentinel-1A data were processed before and after the earthquake to obtain the coseismic deformation field and pre-earthquake deformation rate in the lifting rail direction. Combining the optical interpretation results, the InSAR deformation results and the results of the field survey, it is shown that the deformation trends of the LOS in the ascending and descending orbits to the south and north of the orbit show obvious opposite trends. The surface shape variables are −50 to 45 cm and −65 to 72 cm. The deformation rate before the earthquake reached 25 cm/year. The characteristics of the deformation field show that the earthquake was dominated by thrust, and the coseismic deformation field ruptured along the WNW‒ESE direction, with a rupture length of approximately 33 km. The earthquake caused disasters in Menyuan, Qilian, and Gangcha Counties in Haibei Prefecture. The entire earthquake areas affected by the 10 mm, 20 cm, and 50 cm deformation magnitudes were 975.14, 321.10, and 38.55 km2, respectively. There are two main disaster-affected townships within 20 km, namely, Sujitan Township and Huangcheng Mongolian Township. There are four main disaster-affected townships within 50 km, namely, Sujitan Township, Huangcheng Mongolian Township, Qingshizui Township, and Haomen Township. Through on-the-spot investigation, a total of 5,831 people in 1,662 households in Menyuan, Qilian, and Gangcha Counties of Haibei Prefecture were affected, 16 households with 65 people were urgently transferred and resettled (including 45 people from 12 households in Huangcheng Township and 20 people from 4 households in Yintian Township), 9 people were injured and no deaths occurred, 217 houses were severely damaged, 3,835 houses were generally damaged, 6 barns collapsed, and 145 houses were generally damaged, the water supply and drainage pipe network was damaged for 15 km, the heating pipe network was damaged for 3.96 km, the provincial road was damaged for 3.3 km, the rural road was damaged for 8 km, 3 bridges were damaged, and 17 culverts were damaged. There was one hidden danger point within the geological disaster, and the management and protection stations of Qilian Mountain National Park were damaged to varying degrees.

  1. Funding information: The work was supported by Natural Science Foundation of Qinghai Province (2021-ZJ-909) and Philosophy and Social Science Foundation of Qinghai Province (22Q071).

  2. Author contributions: Zhang Nana designed the experiments and drafted the manuscript. Data processing and visualization were performed by Li Long, Li Jun, Jiang Gang, Ge Yuejing, and Ma Yujun.

  3. Conflict of interest: The authors declare that there are no conflicts of interest.

  4. Data availability statement: The datasets generated during analyzed during the current study are available from the corresponding author on reasonable request.

References

[1] Zhong J, Feng X, Wang B, Zhou Z, Yan R. Analysis on anomalous characteristics of underground fluid before the Menyuan, Qinghai Mg6.9 earthquake in 2022. Earthq Res. 2022;45(2):308–17.Search in Google Scholar

[2] Zhao LQ, Sun XY, Zhan Y, Yang HB, Wang QL, Hao M, et al. The seismogenic model of the Menyuan Ms6.9 earthquake on January 8, 2022, Qinghai province and segmented extensional characteristics of the Lenglongling fault. J Geophysics. 2022;65(4):1536–46.Search in Google Scholar

[3] Champatiray PK, Parvaiz I, Jayangondaperumal R, Thakur VC, Dadhwal VK. Earthquake-triggered landslide modeling and deformation analysis related to 2005 Kashmir earthquake using satellite imagery. Integr Disaster Sci Manag. 2018;15(2):433–50.10.1016/B978-0-12-812056-9.00026-9Search in Google Scholar

[4] Wang K, Fialko Y. Space geodetic observations and models of postseismic deformation due to the 2005 M7.6 Kashmir (Pakistan) earthquake. J Geophys Res. 2014;119(9):7306–18.10.1002/2014JB011122Search in Google Scholar

[5] Yan BD, Ji LY, Jiang FY, Yin HT, Cen QF, Lian KX. Seismogenic structure of Menyuan, Qinghai Ms6.9 earthquake on January 8, 2022 constrained by InSAR Data. J Earthq Eng. 2022;44(2):450–7.Search in Google Scholar

[6] Zhu S, Zhan W, Bao LH, Li JW. Caetenie domomton haceaees eore the Mepyan, Qnghai M6eathquep from GNSS observation data. J Earthq Eng. 2022;44(2):370–9.Search in Google Scholar

[7] Liu ZM, Zhang GW, Liang SS, Zou LY. Spatial migration characteristics of the aftershock sequence of the Menyuan, Qinghai Ms6.9 earthquake in 2022. J Earthq Eng. 2022;44(2):475–87.Search in Google Scholar

[8] Jiang WL. Holocene rupture pattern, seismic recurrence feature of the Lenglongling fault zone and its tectonic implication for the northeast Tibetan Plateau. Int Earthq Dev. 2019;2019(9):46–8.Search in Google Scholar

[9] Guo P, Han Z, Dong S. Surface rupture and slip distribution along the Lenglongling fault in the NE Tibetan Plateau: Implications for faulting behavior. J Asian Earth Sci. 2019;172:190–207.10.1016/j.jseaes.2018.09.008Search in Google Scholar

[10] Xie T, Lin S, Fu LI, Cong F, Zaihui LI, Zou G. Characteristics of the Longling-Ruili fault at the Luxi area and its significance. Geotectonica Et Metallogenia. 2011;35(2):216–20.Search in Google Scholar

[11] Liu LW, Ji LY, Zhao Q. The North-South seismic belt: vertical deformation velocity gradient. Res Earthq Res China. 2017;2(31):22–31.Search in Google Scholar

[12] Huang XM, Yi D, Shu SB, Xie F. Study of the Late Quaternary Slip Rate Along the Northern Segment on the South Branch of the Longling-Ruili Fault. Earthq Res China Engl Ed. 2010;12(4):11–9.Search in Google Scholar

[13] Xu Y, Yang XT, Li ZW, Liu JH. Seismic structure of the Tengchong volcanic area southwest China from local earthquake tomography. J Volcanol Geotherm Res. 2012;239(1):83–91.10.1016/j.jvolgeores.2012.06.017Search in Google Scholar

[14] Peng ZJ, Yan F, Jiang WL, Feng ZB. Activity of the Lenglongling fault system and seismotectonics of the 2016 MS6.4 Menyuan earthquake. Sci China Earth Sci Engl. 2017;60(5):14–21.10.1007/s11430-016-9007-2Search in Google Scholar

[15] Gaudemer Y, Tapponnier P, Meyer B, Peltzer G, Shunmin G, Zhitai C, et al. Partitioning of crustal slip between linked, active faults in the eastern Qilian Shan, and evidence for a major seismic gap, the ‘Tianzhu gap’, on the western Haiyuan Fault, Gansu (China). Geophys J Int. 1995;120(3):599–645.10.1111/j.1365-246X.1995.tb01842.xSearch in Google Scholar

[16] He WG, Liu BC, Yuan DY, Yang M. Research on sliprates of the Leng Long Ling active faultzone. Northwest Seismol J. 2000;22(1):90–7.Search in Google Scholar

[17] Lasserre C, Gaudemer Y, Tapponnier P, Mériaux AS, Van der Woerd J, Daoyang Y, et al. Fast late pleistocene slip rate on the Leng Long Ling segment of the Haiyuan fault, Qinghai, China. J Geophys Res. 2002;207(B11):2276–82.10.1029/2000JB000060Search in Google Scholar

[18] Zheng W, Zhang P, He W, Yuan D, Shao Y, Zheng D, et al. Transformation of displacement between strike-slip and crustal shortening in the northern margin of the Tibetan Plateau: Evidence from decadal GPS measurements and late Quaternary slip rates on faults. Tectonophysics. 2013;584(22):267–80.10.1016/j.tecto.2012.01.006Search in Google Scholar

[19] Yang CS, Zhang Q, Zhao CY, Wang QL, Ji LY. Monitoring land subsidence and fault deformation using the small baseline subset InSAR technique: A case study in the Datong Basin, China. J Geodynamics. 2014;75:34–40.10.1016/j.jog.2014.02.002Search in Google Scholar

[20] Wang H, Ge L, Xu C, Du Z. 3-D coseismic displacement field of the 2005 Kashmir earthquake inferred from satellite radar imagery. Earth Planets Space. 2007;59(5):343–9.10.1186/BF03352694Search in Google Scholar

[21] Wan Y, Shen ZK, Bürgmann R, Sun J, Wang M. Fault geometry and slip distribution of the 2008 Mw 7.9 Wenchuan, China earthquake, inferred from GPS and InSAR measurements. Geophys J Int. 2017;208(2):748–66.10.1093/gji/ggw421Search in Google Scholar

[22] Gabriel AK, Goldstein RM, Zebker HA. Mapping small elevation changes over large areas: Differential radar interferometry. J Geophys Res. 1989;94(B7):9183–91.10.1029/JB094iB07p09183Search in Google Scholar

[23] Massonnet D, Rossi M, Carmona C, Adragna F, Peltzer G, Feigl K, et al. The displacement field of the Landers earthquake mapped by radar interferometry. Nature. 1993;364(6433):138–42.10.1038/364138a0Search in Google Scholar

[24] Berardino P, Fornaro G, Lanari R, Sansosti E. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans Geosci Remote Sens. 2002;40(11):2375–83.10.1109/TGRS.2002.803792Search in Google Scholar

[25] Funning GJ, Parsons B, Wright TJ, Jackson JA, Fielding EJ. Surface displacements and source parameters of the 2003 Bam (Iran) earthquake from Envisat advanced Synthetic Aperture Radar imagery. J Geophys Res. 2005;110(B9):115–23.10.1029/2004JB003338Search in Google Scholar

[26] Serpelloni E, Faccenna C, Spada G, Dong D, Williams SDP. Vertical GPS ground motion rates in the Euro-Mediterranean region: New evidence of velocity gradients at different spatial scales along the Nubia-Eurasia plate boundary. J Geophys Res. 2013;118(11):6003–24.10.1002/2013JB010102Search in Google Scholar

[27] Gu Y, Yuan L, Fan D, You W, Su Y. Seasonal crustal vertical deformation induced by environmental mass loading in mainland China derived from GPS, GRACE and surface loading models. Adv Space Res. 2017;59(1):88–102.10.1016/j.asr.2016.09.008Search in Google Scholar

[28] Schneevoigt NJ, Sund M, Bogren W, Kääb A, Weydahl DJ. Glacier displacement on comfortlessbreen, Svalbard, using 2-pass differential SAR interferometry (DInSAR) with a digital elevation model. Polar Rec. 2011;48(1):17–25.10.1017/S0032247411000453Search in Google Scholar

[29] Tizzani P, Berardino P, Casu F, Euillades P, Manzo M, Ricciardi G, et al. Surface deformation of Long Valley Caldera and Mono Basin, California, investigated with the SBAS-InSAR approach. Remote Sens Environ. 2007;108(3):277–89.10.1016/j.rse.2006.11.015Search in Google Scholar

[30] Yang F, Tang MG. Analysis of deformation characteristics and stability of Shuping landslide under impoundment of three gorges reservoir. Water Resour Power. 2017;85(3):142–5.Search in Google Scholar

[31] Dai KR, Zhuo GC, Qiang X, Li ZH, Li WL, Guan W. Tracing the pre-failure two-dimensional surface displacements of Nanyu landslide, Gansu province with radar interferometry. J Wuhan Univ Inf Sci Ed. 2019;44(12):1778–86.Search in Google Scholar

[32] Casagli N, Catani F, Del Ventisette C, Luzi G. Monitoring, prediction, and early warning using ground-based radar interferometry. Landslides. 2010;7(3):291–301.10.1007/s10346-010-0215-ySearch in Google Scholar

[33] Casagli N, Cigna F, Bianchini S, Hölbling D, Füreder P, Righini G, et al. Landslide mapping and monitoring by using radar and optical remote sensing: Examples from the EC-FP7 project SAFER. Remote Sens App Soc Environ. 2016;4:92–108.10.1016/j.rsase.2016.07.001Search in Google Scholar

[34] Chvez TM, Nguyen DT. Efficient algorithms for identifying loop formation and computing θ value for solving minimum cost flow network problems. WSEAS Trans Circuits Syst. 2021;20:107–17.10.37394/23201.2021.20.14Search in Google Scholar

[35] Tobita M, Nishimura T, Kobayashi T, Hao KX, Shindo Y. Estimation of coseismic deformation and a fault model of the 2010 Yushu earthquake using PALSAR interferometry data. Earth Planet Sci Lett. 2011;307(3):430–8.10.1016/j.epsl.2011.05.017Search in Google Scholar

[36] Zhao D, Qu C, Shan X, Gong W, Zhang Y, Zhang G. InSAR and GPS derived coseismic deformation and fault model of the 2017 Ms7.0 Jiuzhaigou earthquake in the Northeast Bayanhar block. Tectonophysics. 2018;726:86–99.10.1016/j.tecto.2018.01.026Search in Google Scholar

[37] Li Y, Gan W, Wang Y, Chen W, Liang S, Zhang K, et al. Seismogenic structure of the 2016 Ms6.4 Menyuan earthquake and its effect on the Tianzhu seismic gap. Geodesy Geodynamics. 2016;7(4):230–36.10.1016/j.geog.2016.07.002Search in Google Scholar

[38] Chen J, Zhao L, Wu H. Study on the focal mechanism of the M5.1 badong earthquake in Hubei. Geodesy Geodynamics. 2014;5(1):47–54.10.3724/SP.J.1246.2014.01047Search in Google Scholar

[39] Joussineau GD, Mutlu O, Aydin A, Pollard DD. Characterization of strike-slip fault–splay relationships in sandstone. J Struct Geol. 2007;29(11):1831–42.10.1016/j.jsg.2007.08.006Search in Google Scholar

[40] Salvini F, Storti F. The distribution of deformation in parallel fault-related folds with migrating axial surfaces: Comparison between fault-propagation and fault-bend folding. J Struct Geol. 2001;23(1):25–32.10.1016/S0191-8141(00)00081-XSearch in Google Scholar
Received: 2023-06-02
Revised: 2023-12-04
Accepted: 2023-12-11
Published Online: 2024-02-15

© 2024 the author(s), published by De Gruyter

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

Articles in the same Issue

  1. Regular Articles
  2. Theoretical magnetotelluric response of stratiform earth consisting of alternative homogeneous and transitional layers
  3. The research of common drought indexes for the application to the drought monitoring in the region of Jin Sha river
  4. Evolutionary game analysis of government, businesses, and consumers in high-standard farmland low-carbon construction
  5. On the use of low-frequency passive seismic as a direct hydrocarbon indicator: A case study at Banyubang oil field, Indonesia
  6. Water transportation planning in connection with extreme weather conditions; case study – Port of Novi Sad, Serbia
  7. Zircon U–Pb ages of the Paleozoic volcaniclastic strata in the Junggar Basin, NW China
  8. Monitoring of mangrove forests vegetation based on optical versus microwave data: A case study western coast of Saudi Arabia
  9. Microfacies analysis of marine shale: A case study of the shales of the Wufeng–Longmaxi formation in the western Chongqing, Sichuan Basin, China
  10. Multisource remote sensing image fusion processing in plateau seismic region feature information extraction and application analysis – An example of the Menyuan Ms6.9 earthquake on January 8, 2022
  11. Identification of magnetic mineralogy and paleo-flow direction of the Miocene-quaternary volcanic products in the north of Lake Van, Eastern Turkey
  12. Impact of fully rotating steel casing bored pile on adjacent tunnels
  13. Adolescents’ consumption intentions toward leisure tourism in high-risk leisure environments in riverine areas
  14. Petrogenesis of Jurassic granitic rocks in South China Block: Implications for events related to subduction of Paleo-Pacific plate
  15. Differences in urban daytime and night block vitality based on mobile phone signaling data: A case study of Kunming’s urban district
  16. Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan
  17. Integrated geophysical approach for detection and size-geometry characterization of a multiscale karst system in carbonate units, semiarid Brazil
  18. Spatial and temporal changes in ecosystem services value and analysis of driving factors in the Yangtze River Delta Region
  19. Deep fault sliding rates for Ka-Ping block of Xinjiang based on repeating earthquakes
  20. Improved deep learning segmentation of outdoor point clouds with different sampling strategies and using intensities
  21. Platform margin belt structure and sedimentation characteristics of Changxing Formation reefs on both sides of the Kaijiang-Liangping trough, eastern Sichuan Basin, China
  22. Enhancing attapulgite and cement-modified loess for effective landfill lining: A study on seepage prevention and Cu/Pb ion adsorption
  23. Flood risk assessment, a case study in an arid environment of Southeast Morocco
  24. Lower limits of physical properties and classification evaluation criteria of the tight reservoir in the Ahe Formation in the Dibei Area of the Kuqa depression
  25. Evaluation of Viaducts’ contribution to road network accessibility in the Yunnan–Guizhou area based on the node deletion method
  26. Permian tectonic switch of the southern Central Asian Orogenic Belt: Constraints from magmatism in the southern Alxa region, NW China
  27. Element geochemical differences in lower Cambrian black shales with hydrothermal sedimentation in the Yangtze block, South China
  28. Three-dimensional finite-memory quasi-Newton inversion of the magnetotelluric based on unstructured grids
  29. Obliquity-paced summer monsoon from the Shilou red clay section on the eastern Chinese Loess Plateau
  30. Classification and logging identification of reservoir space near the upper Ordovician pinch-out line in Tahe Oilfield
  31. Ultra-deep channel sand body target recognition method based on improved deep learning under UAV cluster
  32. New formula to determine flyrock distance on sedimentary rocks with low strength
  33. Assessing the ecological security of tourism in Northeast China
  34. Effective reservoir identification and sweet spot prediction in Chang 8 Member tight oil reservoirs in Huanjiang area, Ordos Basin
  35. Detecting heterogeneity of spatial accessibility to sports facilities for adolescents at fine scale: A case study in Changsha, China
  36. Effects of freeze–thaw cycles on soil nutrients by soft rock and sand remodeling
  37. Vibration prediction with a method based on the absorption property of blast-induced seismic waves: A case study
  38. A new look at the geodynamic development of the Ediacaran–early Cambrian forearc basalts of the Tannuola-Khamsara Island Arc (Central Asia, Russia): Conclusions from geological, geochemical, and Nd-isotope data
  39. Spatio-temporal analysis of the driving factors of urban land use expansion in China: A study of the Yangtze River Delta region
  40. Selection of Euler deconvolution solutions using the enhanced horizontal gradient and stable vertical differentiation
  41. Phase change of the Ordovician hydrocarbon in the Tarim Basin: A case study from the Halahatang–Shunbei area
  42. Using interpretative structure model and analytical network process for optimum site selection of airport locations in Delta Egypt
  43. Geochemistry of magnetite from Fe-skarn deposits along the central Loei Fold Belt, Thailand
  44. Functional typology of settlements in the Srem region, Serbia
  45. Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies
  46. Addressing incomplete tile phenomena in image tiling: Introducing the grid six-intersection model
  47. Evaluation and control model for resilience of water resource building system based on fuzzy comprehensive evaluation method and its application
  48. MIF and AHP methods for delineation of groundwater potential zones using remote sensing and GIS techniques in Tirunelveli, Tenkasi District, India
  49. New database for the estimation of dynamic coefficient of friction of snow
  50. Measuring urban growth dynamics: A study in Hue city, Vietnam
  51. Comparative models of support-vector machine, multilayer perceptron, and decision tree ‎predication approaches for landslide ‎susceptibility analysis
  52. Experimental study on the influence of clay content on the shear strength of silty soil and mechanism analysis
  53. Geosite assessment as a contribution to the sustainable development of Babušnica, Serbia
  54. Using fuzzy analytical hierarchy process for road transportation services management based on remote sensing and GIS technology
  55. Accumulation mechanism of multi-type unconventional oil and gas reservoirs in Northern China: Taking Hari Sag of the Yin’e Basin as an example
  56. TOC prediction of source rocks based on the convolutional neural network and logging curves – A case study of Pinghu Formation in Xihu Sag
  57. A method for fast detection of wind farms from remote sensing images using deep learning and geospatial analysis
  58. Spatial distribution and driving factors of karst rocky desertification in Southwest China based on GIS and geodetector
  59. Physicochemical and mineralogical composition studies of clays from Share and Tshonga areas, Northern Bida Basin, Nigeria: Implications for Geophagia
  60. Geochemical sedimentary records of eutrophication and environmental change in Chaohu Lake, East China
  61. Research progress of freeze–thaw rock using bibliometric analysis
  62. Mixed irrigation affects the composition and diversity of the soil bacterial community
  63. Examining the swelling potential of cohesive soils with high plasticity according to their index properties using GIS
  64. Geological genesis and identification of high-porosity and low-permeability sandstones in the Cretaceous Bashkirchik Formation, northern Tarim Basin
  65. Usability of PPGIS tools exemplified by geodiscussion – a tool for public participation in shaping public space
  66. Efficient development technology of Upper Paleozoic Lower Shihezi tight sandstone gas reservoir in northeastern Ordos Basin
  67. Assessment of soil resources of agricultural landscapes in Turkestan region of the Republic of Kazakhstan based on agrochemical indexes
  68. Evaluating the impact of DEM interpolation algorithms on relief index for soil resource management
  69. Petrogenetic relationship between plutonic and subvolcanic rocks in the Jurassic Shuikoushan complex, South China
  70. A novel workflow for shale lithology identification – A case study in the Gulong Depression, Songliao Basin, China
  71. Characteristics and main controlling factors of dolomite reservoirs in Fei-3 Member of Feixianguan Formation of Lower Triassic, Puguang area
  72. Impact of high-speed railway network on county-level accessibility and economic linkage in Jiangxi Province, China: A spatio-temporal data analysis
  73. Estimation model of wild fractional vegetation cover based on RGB vegetation index and its application
  74. Lithofacies, petrography, and geochemistry of the Lamphun oceanic plate stratigraphy: As a record of the subduction history of Paleo-Tethys in Chiang Mai-Chiang Rai Suture Zone of Thailand
  75. Structural features and tectonic activity of the Weihe Fault, central China
  76. Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin
  77. Structural detachment influences the shale gas preservation in the Wufeng-Longmaxi Formation, Northern Guizhou Province
  78. Distribution law of Chang 7 Member tight oil in the western Ordos Basin based on geological, logging and numerical simulation techniques
  79. Evaluation of alteration in the geothermal province west of Cappadocia, Türkiye: Mineralogical, petrographical, geochemical, and remote sensing data
  80. Numerical modeling of site response at large strains with simplified nonlinear models: Application to Lotung seismic array
  81. Quantitative characterization of granite failure intensity under dynamic disturbance from energy standpoint
  82. Characteristics of debris flow dynamics and prediction of the hazardous area in Bangou Village, Yanqing District, Beijing, China
  83. Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method
  84. Statistical comparison analysis of different real-time kinematic methods for the development of photogrammetric products: CORS-RTK, CORS-RTK + PPK, RTK-DRTK2, and RTK + DRTK2 + GCP
  85. Hydrogeological mapping of fracture networks using earth observation data to improve rainfall–runoff modeling in arid mountains, Saudi Arabia
  86. Petrography and geochemistry of pegmatite and leucogranite of Ntega-Marangara area, Burundi, in relation to rare metal mineralisation
  87. Prediction of formation fracture pressure based on reinforcement learning and XGBoost
  88. Hazard zonation for potential earthquake-induced landslide in the eastern East Kunlun fault zone
  89. Monitoring water infiltration in multiple layers of sandstone coal mining model with cracks using ERT
  90. Study of the patterns of ice lake variation and the factors influencing these changes in the western Nyingchi area
  91. Productive conservation at the landslide prone area under the threat of rapid land cover changes
  92. Sedimentary processes and patterns in deposits corresponding to freshwater lake-facies of hyperpycnal flow – An experimental study based on flume depositional simulations
  93. Study on time-dependent injectability evaluation of mudstone considering the self-healing effect
  94. Detection of objects with diverse geometric shapes in GPR images using deep-learning methods
  95. Behavior of trace metals in sedimentary cores from marine and lacustrine environments in Algeria
  96. Spatiotemporal variation pattern and spatial coupling relationship between NDVI and LST in Mu Us Sandy Land
  97. Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin
  98. Diagenesis of marine-continental transitional shale from the Upper Permian Longtan Formation in southern Sichuan Basin, China
  99. Vertical high-velocity structures and seismic activity in western Shandong Rise, China: Case study inspired by double-difference seismic tomography
  100. Spatial coupling relationship between metamorphic core complex and gold deposits: Constraints from geophysical electromagnetics
  101. Disparities in the geospatial allocation of public facilities from the perspective of living circles
  102. Research on spatial correlation structure of war heritage based on field theory. A case study of Jinzhai County, China
  103. Formation mechanisms of Qiaoba-Zhongdu Danxia landforms in southwestern Sichuan Province, China
  104. Magnetic data interpretation: Implication for structure and hydrocarbon potentiality at Delta Wadi Diit, Southeastern Egypt
  105. Deeply buried clastic rock diagenesis evolution mechanism of Dongdaohaizi sag in the center of Junggar fault basin, Northwest China
  106. Application of LS-RAPID to simulate the motion of two contrasting landslides triggered by earthquakes
  107. The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023
  108. The critical role of c and φ in ensuring stability: A study on rockfill dams
  109. Evidence of late quaternary activity of the Weining-Shuicheng Fault in Guizhou, China
  110. Extreme hydroclimatic events and response of vegetation in the eastern QTP since 10 ka
  111. Spatial–temporal effect of sea–land gradient on landscape pattern and ecological risk in the coastal zone: A case study of Dalian City
  112. Study on the influence mechanism of land use on carbon storage under multiple scenarios: A case study of Wenzhou
  113. A new method for identifying reservoir fluid properties based on well logging data: A case study from PL block of Bohai Bay Basin, North China
  114. Comparison between thermal models across the Middle Magdalena Valley, Eastern Cordillera, and Eastern Llanos basins in Colombia
  115. Mineralogical and elemental analysis of Kazakh coals from three mines: Preliminary insights from mode of occurrence to environmental impacts
  116. Chlorite-induced porosity evolution in multi-source tight sandstone reservoirs: A case study of the Shaximiao Formation in western Sichuan Basin
  117. Predicting stability factors for rotational failures in earth slopes and embankments using artificial intelligence techniques
  118. Origin of Late Cretaceous A-type granitoids in South China: Response to the rollback and retreat of the Paleo-Pacific plate
  119. Modification of dolomitization on reservoir spaces in reef–shoal complex: A case study of Permian Changxing Formation, Sichuan Basin, SW China
  120. Geological characteristics of the Daduhe gold belt, western Sichuan, China: Implications for exploration
  121. Rock physics model for deep coal-bed methane reservoir based on equivalent medium theory: A case study of Carboniferous-Permian in Eastern Ordos Basin
  122. Enhancing the total-field magnetic anomaly using the normalized source strength
  123. Shear wave velocity profiling of Riyadh City, Saudi Arabia, utilizing the multi-channel analysis of surface waves method
  124. Effect of coal facies on pore structure heterogeneity of coal measures: Quantitative characterization and comparative study
  125. Inversion method of organic matter content of different types of soils in black soil area based on hyperspectral indices
  126. Detection of seepage zones in artificial levees: A case study at the Körös River, Hungary
  127. Tight sandstone fluid detection technology based on multi-wave seismic data
  128. Characteristics and control techniques of soft rock tunnel lining cracks in high geo-stress environments: Case study of Wushaoling tunnel group
  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
  131. Multi-scenario territorial spatial simulation and dynamic changes: A case study of Jilin Province in China from 1985 to 2030
  132. Review Articles
  133. Major ascidian species with negative impacts on bivalve aquaculture: Current knowledge and future research aims
  134. Prediction and assessment of meteorological drought in southwest China using long short-term memory model
  135. Communication
  136. Essential questions in earth and geosciences according to large language models
  137. Erratum
  138. Erratum to “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan”
  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
  140. Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)
  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
  142. Determinants influencing tourists’ willingness to visit Türkiye – Impact of earthquake hazards on Serbian visitors’ preferences
  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
https://doi.org/10.1515/geo-2022-0599
Keywords for this article
DOM; DEM; D-InSAR; SBAS-InSAR; lifting rail fusion; fusion processing
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Theoretical magnetotelluric response of stratiform earth consisting of alternative homogeneous and transitional layers
  3. The research of common drought indexes for the application to the drought monitoring in the region of Jin Sha river
  4. Evolutionary game analysis of government, businesses, and consumers in high-standard farmland low-carbon construction
  5. On the use of low-frequency passive seismic as a direct hydrocarbon indicator: A case study at Banyubang oil field, Indonesia
  6. Water transportation planning in connection with extreme weather conditions; case study – Port of Novi Sad, Serbia
  7. Zircon U–Pb ages of the Paleozoic volcaniclastic strata in the Junggar Basin, NW China
  8. Monitoring of mangrove forests vegetation based on optical versus microwave data: A case study western coast of Saudi Arabia
  9. Microfacies analysis of marine shale: A case study of the shales of the Wufeng–Longmaxi formation in the western Chongqing, Sichuan Basin, China
  10. Multisource remote sensing image fusion processing in plateau seismic region feature information extraction and application analysis – An example of the Menyuan Ms6.9 earthquake on January 8, 2022
  11. Identification of magnetic mineralogy and paleo-flow direction of the Miocene-quaternary volcanic products in the north of Lake Van, Eastern Turkey
  12. Impact of fully rotating steel casing bored pile on adjacent tunnels
  13. Adolescents’ consumption intentions toward leisure tourism in high-risk leisure environments in riverine areas
  14. Petrogenesis of Jurassic granitic rocks in South China Block: Implications for events related to subduction of Paleo-Pacific plate
  15. Differences in urban daytime and night block vitality based on mobile phone signaling data: A case study of Kunming’s urban district
  16. Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan
  17. Integrated geophysical approach for detection and size-geometry characterization of a multiscale karst system in carbonate units, semiarid Brazil
  18. Spatial and temporal changes in ecosystem services value and analysis of driving factors in the Yangtze River Delta Region
  19. Deep fault sliding rates for Ka-Ping block of Xinjiang based on repeating earthquakes
  20. Improved deep learning segmentation of outdoor point clouds with different sampling strategies and using intensities
  21. Platform margin belt structure and sedimentation characteristics of Changxing Formation reefs on both sides of the Kaijiang-Liangping trough, eastern Sichuan Basin, China
  22. Enhancing attapulgite and cement-modified loess for effective landfill lining: A study on seepage prevention and Cu/Pb ion adsorption
  23. Flood risk assessment, a case study in an arid environment of Southeast Morocco
  24. Lower limits of physical properties and classification evaluation criteria of the tight reservoir in the Ahe Formation in the Dibei Area of the Kuqa depression
  25. Evaluation of Viaducts’ contribution to road network accessibility in the Yunnan–Guizhou area based on the node deletion method
  26. Permian tectonic switch of the southern Central Asian Orogenic Belt: Constraints from magmatism in the southern Alxa region, NW China
  27. Element geochemical differences in lower Cambrian black shales with hydrothermal sedimentation in the Yangtze block, South China
  28. Three-dimensional finite-memory quasi-Newton inversion of the magnetotelluric based on unstructured grids
  29. Obliquity-paced summer monsoon from the Shilou red clay section on the eastern Chinese Loess Plateau
  30. Classification and logging identification of reservoir space near the upper Ordovician pinch-out line in Tahe Oilfield
  31. Ultra-deep channel sand body target recognition method based on improved deep learning under UAV cluster
  32. New formula to determine flyrock distance on sedimentary rocks with low strength
  33. Assessing the ecological security of tourism in Northeast China
  34. Effective reservoir identification and sweet spot prediction in Chang 8 Member tight oil reservoirs in Huanjiang area, Ordos Basin
  35. Detecting heterogeneity of spatial accessibility to sports facilities for adolescents at fine scale: A case study in Changsha, China
  36. Effects of freeze–thaw cycles on soil nutrients by soft rock and sand remodeling
  37. Vibration prediction with a method based on the absorption property of blast-induced seismic waves: A case study
  38. A new look at the geodynamic development of the Ediacaran–early Cambrian forearc basalts of the Tannuola-Khamsara Island Arc (Central Asia, Russia): Conclusions from geological, geochemical, and Nd-isotope data
  39. Spatio-temporal analysis of the driving factors of urban land use expansion in China: A study of the Yangtze River Delta region
  40. Selection of Euler deconvolution solutions using the enhanced horizontal gradient and stable vertical differentiation
  41. Phase change of the Ordovician hydrocarbon in the Tarim Basin: A case study from the Halahatang–Shunbei area
  42. Using interpretative structure model and analytical network process for optimum site selection of airport locations in Delta Egypt
  43. Geochemistry of magnetite from Fe-skarn deposits along the central Loei Fold Belt, Thailand
  44. Functional typology of settlements in the Srem region, Serbia
  45. Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies
  46. Addressing incomplete tile phenomena in image tiling: Introducing the grid six-intersection model
  47. Evaluation and control model for resilience of water resource building system based on fuzzy comprehensive evaluation method and its application
  48. MIF and AHP methods for delineation of groundwater potential zones using remote sensing and GIS techniques in Tirunelveli, Tenkasi District, India
  49. New database for the estimation of dynamic coefficient of friction of snow
  50. Measuring urban growth dynamics: A study in Hue city, Vietnam
  51. Comparative models of support-vector machine, multilayer perceptron, and decision tree ‎predication approaches for landslide ‎susceptibility analysis
  52. Experimental study on the influence of clay content on the shear strength of silty soil and mechanism analysis
  53. Geosite assessment as a contribution to the sustainable development of Babušnica, Serbia
  54. Using fuzzy analytical hierarchy process for road transportation services management based on remote sensing and GIS technology
  55. Accumulation mechanism of multi-type unconventional oil and gas reservoirs in Northern China: Taking Hari Sag of the Yin’e Basin as an example
  56. TOC prediction of source rocks based on the convolutional neural network and logging curves – A case study of Pinghu Formation in Xihu Sag
  57. A method for fast detection of wind farms from remote sensing images using deep learning and geospatial analysis
  58. Spatial distribution and driving factors of karst rocky desertification in Southwest China based on GIS and geodetector
  59. Physicochemical and mineralogical composition studies of clays from Share and Tshonga areas, Northern Bida Basin, Nigeria: Implications for Geophagia
  60. Geochemical sedimentary records of eutrophication and environmental change in Chaohu Lake, East China
  61. Research progress of freeze–thaw rock using bibliometric analysis
  62. Mixed irrigation affects the composition and diversity of the soil bacterial community
  63. Examining the swelling potential of cohesive soils with high plasticity according to their index properties using GIS
  64. Geological genesis and identification of high-porosity and low-permeability sandstones in the Cretaceous Bashkirchik Formation, northern Tarim Basin
  65. Usability of PPGIS tools exemplified by geodiscussion – a tool for public participation in shaping public space
  66. Efficient development technology of Upper Paleozoic Lower Shihezi tight sandstone gas reservoir in northeastern Ordos Basin
  67. Assessment of soil resources of agricultural landscapes in Turkestan region of the Republic of Kazakhstan based on agrochemical indexes
  68. Evaluating the impact of DEM interpolation algorithms on relief index for soil resource management
  69. Petrogenetic relationship between plutonic and subvolcanic rocks in the Jurassic Shuikoushan complex, South China
  70. A novel workflow for shale lithology identification – A case study in the Gulong Depression, Songliao Basin, China
  71. Characteristics and main controlling factors of dolomite reservoirs in Fei-3 Member of Feixianguan Formation of Lower Triassic, Puguang area
  72. Impact of high-speed railway network on county-level accessibility and economic linkage in Jiangxi Province, China: A spatio-temporal data analysis
  73. Estimation model of wild fractional vegetation cover based on RGB vegetation index and its application
  74. Lithofacies, petrography, and geochemistry of the Lamphun oceanic plate stratigraphy: As a record of the subduction history of Paleo-Tethys in Chiang Mai-Chiang Rai Suture Zone of Thailand
  75. Structural features and tectonic activity of the Weihe Fault, central China
  76. Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin
  77. Structural detachment influences the shale gas preservation in the Wufeng-Longmaxi Formation, Northern Guizhou Province
  78. Distribution law of Chang 7 Member tight oil in the western Ordos Basin based on geological, logging and numerical simulation techniques
  79. Evaluation of alteration in the geothermal province west of Cappadocia, Türkiye: Mineralogical, petrographical, geochemical, and remote sensing data
  80. Numerical modeling of site response at large strains with simplified nonlinear models: Application to Lotung seismic array
  81. Quantitative characterization of granite failure intensity under dynamic disturbance from energy standpoint
  82. Characteristics of debris flow dynamics and prediction of the hazardous area in Bangou Village, Yanqing District, Beijing, China
  83. Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method
  84. Statistical comparison analysis of different real-time kinematic methods for the development of photogrammetric products: CORS-RTK, CORS-RTK + PPK, RTK-DRTK2, and RTK + DRTK2 + GCP
  85. Hydrogeological mapping of fracture networks using earth observation data to improve rainfall–runoff modeling in arid mountains, Saudi Arabia
  86. Petrography and geochemistry of pegmatite and leucogranite of Ntega-Marangara area, Burundi, in relation to rare metal mineralisation
  87. Prediction of formation fracture pressure based on reinforcement learning and XGBoost
  88. Hazard zonation for potential earthquake-induced landslide in the eastern East Kunlun fault zone
  89. Monitoring water infiltration in multiple layers of sandstone coal mining model with cracks using ERT
  90. Study of the patterns of ice lake variation and the factors influencing these changes in the western Nyingchi area
  91. Productive conservation at the landslide prone area under the threat of rapid land cover changes
  92. Sedimentary processes and patterns in deposits corresponding to freshwater lake-facies of hyperpycnal flow – An experimental study based on flume depositional simulations
  93. Study on time-dependent injectability evaluation of mudstone considering the self-healing effect
  94. Detection of objects with diverse geometric shapes in GPR images using deep-learning methods
  95. Behavior of trace metals in sedimentary cores from marine and lacustrine environments in Algeria
  96. Spatiotemporal variation pattern and spatial coupling relationship between NDVI and LST in Mu Us Sandy Land
  97. Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin
  98. Diagenesis of marine-continental transitional shale from the Upper Permian Longtan Formation in southern Sichuan Basin, China
  99. Vertical high-velocity structures and seismic activity in western Shandong Rise, China: Case study inspired by double-difference seismic tomography
  100. Spatial coupling relationship between metamorphic core complex and gold deposits: Constraints from geophysical electromagnetics
  101. Disparities in the geospatial allocation of public facilities from the perspective of living circles
  102. Research on spatial correlation structure of war heritage based on field theory. A case study of Jinzhai County, China
  103. Formation mechanisms of Qiaoba-Zhongdu Danxia landforms in southwestern Sichuan Province, China
  104. Magnetic data interpretation: Implication for structure and hydrocarbon potentiality at Delta Wadi Diit, Southeastern Egypt
  105. Deeply buried clastic rock diagenesis evolution mechanism of Dongdaohaizi sag in the center of Junggar fault basin, Northwest China
  106. Application of LS-RAPID to simulate the motion of two contrasting landslides triggered by earthquakes
  107. The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023
  108. The critical role of c and φ in ensuring stability: A study on rockfill dams
  109. Evidence of late quaternary activity of the Weining-Shuicheng Fault in Guizhou, China
  110. Extreme hydroclimatic events and response of vegetation in the eastern QTP since 10 ka
  111. Spatial–temporal effect of sea–land gradient on landscape pattern and ecological risk in the coastal zone: A case study of Dalian City
  112. Study on the influence mechanism of land use on carbon storage under multiple scenarios: A case study of Wenzhou
  113. A new method for identifying reservoir fluid properties based on well logging data: A case study from PL block of Bohai Bay Basin, North China
  114. Comparison between thermal models across the Middle Magdalena Valley, Eastern Cordillera, and Eastern Llanos basins in Colombia
  115. Mineralogical and elemental analysis of Kazakh coals from three mines: Preliminary insights from mode of occurrence to environmental impacts
  116. Chlorite-induced porosity evolution in multi-source tight sandstone reservoirs: A case study of the Shaximiao Formation in western Sichuan Basin
  117. Predicting stability factors for rotational failures in earth slopes and embankments using artificial intelligence techniques
  118. Origin of Late Cretaceous A-type granitoids in South China: Response to the rollback and retreat of the Paleo-Pacific plate
  119. Modification of dolomitization on reservoir spaces in reef–shoal complex: A case study of Permian Changxing Formation, Sichuan Basin, SW China
  120. Geological characteristics of the Daduhe gold belt, western Sichuan, China: Implications for exploration
  121. Rock physics model for deep coal-bed methane reservoir based on equivalent medium theory: A case study of Carboniferous-Permian in Eastern Ordos Basin
  122. Enhancing the total-field magnetic anomaly using the normalized source strength
  123. Shear wave velocity profiling of Riyadh City, Saudi Arabia, utilizing the multi-channel analysis of surface waves method
  124. Effect of coal facies on pore structure heterogeneity of coal measures: Quantitative characterization and comparative study
  125. Inversion method of organic matter content of different types of soils in black soil area based on hyperspectral indices
  126. Detection of seepage zones in artificial levees: A case study at the Körös River, Hungary
  127. Tight sandstone fluid detection technology based on multi-wave seismic data
  128. Characteristics and control techniques of soft rock tunnel lining cracks in high geo-stress environments: Case study of Wushaoling tunnel group
  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
  131. Multi-scenario territorial spatial simulation and dynamic changes: A case study of Jilin Province in China from 1985 to 2030
  132. Review Articles
  133. Major ascidian species with negative impacts on bivalve aquaculture: Current knowledge and future research aims
  134. Prediction and assessment of meteorological drought in southwest China using long short-term memory model
  135. Communication
  136. Essential questions in earth and geosciences according to large language models
  137. Erratum
  138. Erratum to “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan”
  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
  140. Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)
  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
  142. Determinants influencing tourists’ willingness to visit Türkiye – Impact of earthquake hazards on Serbian visitors’ preferences
  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 7.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/geo-2022-0599/html
Scroll to top button