Surface Subsidence Monitoring Research Articles

Deformation-Adapted Spatial Domain Filtering Algorithm for UAV Mining Subsidence Monitoring

Underground coal mining induces surface subsidence, leading to disasters such as damage to buildings and infrastructure, landslides, and surface water accumulation. Preventing and controlling disasters in subsidence areas and reutilizing land depend on understanding subsidence regularity and obtaining surface subsidence monitoring data. These data are crucial for the reutilization of regional land resources and disaster prevention and control. Subsidence hazards are also a key constraint to mine development. Recently, with the rapid advancement of UAV technology, the use of UAV photogrammetry for surface subsidence monitoring has become a significant trend in this field. The periodic imagery data quickly acquired by UAV are used to construct DEM through point cloud filtering. Then, surface subsidence information is obtained by differencing DEM from different periods. However, due to the accuracy limitations inherent in UAV photogrammetry, the subsidence data obtained through this method are characterized by errors, making it challenging to achieve high-precision ground surface subsidence monitoring. Therefore, this paper proposes a spatial domain filtering algorithm for UAV photogrammetry combined with surface deformation caused by coal mining based on the surface subsidence induced by coal mining and combined with the characteristics of the surface change. This algorithm significantly reduces random error in the differential DEM, achieving high-precision ground subsidence monitoring using UAV. Simulation and field test results show that the surface subsidence elevation errors obtained in the simulation tests are reduced by more than 50% compared to conventional methods. In field tests, this method reduced surface subsidence elevation errors by 39%. The monitoring error for surface subsidence was as low as 8 mm compared to leveling survey data. This method offers a new technical pathway for high-precision surface subsidence monitoring in mining areas using UAV photogrammetry.

Research on reuse of shield soil dregs on synchronous grouting materials and its application

Shield tunnel construction will produce a large amount of soil dregs. Transporting soil dregs directly to landfills raises costs and CO2 emissions. In this paper, the shield tunnel of Intercity from Shenzhen to Huizhou is taken as an example, based on the special treating agent of shield soil dregs, the soil dregs were prepared as synchronous grouting material. The working performance and mechanical properties of soil dregs grouting material were studied. The optimal dosage of the treating agent was proposed, and the curing mechanism of the soil dregs synchronous grouting material was analyzed. The soil dregs grouting material was applied to practical engineering, and the application effect was verified by surface subsidence monitoring. The results showed that the setting time, bleeding rate, stone rate and compressive strength of the soil dregs grouting material were better than cement mortar grouting material. The treating agent realized the cementation of soil particles through the synergistic effect of epoxy resin and nano-SiO2, and the optimum dosage is 0.02 % of the solid component. The maximum surface settlement of the soil dregs grouting construction section was 10 mm, which is smaller than the maximum surface settlement of the cement mortar grouting section. Through the life cycle assessment (LCA) method, the CO2 emission reduction capacity of the soil dregs synchronous grouting material was analyzed. The soil dregs grouting material reduced the production and transportation of fly ash and bentonite, and reduces the outward transportation of soil dregs. The use of 1 t soil dregs synchronous grouting material can reduce 59.05 kg CO2 emission.

Prediction Parameters for Mining Subsidence Based on Interferometric Synthetic Aperture Radar and Unmanned Aerial Vehicle Collaborative Monitoring

Coal mining induces surface subsidence, making rapid and precise monitoring of this subsidence a key area of current research. To address the limitations of D-InSAR technology in capturing large-gradient deformations in the central subsidence basin and the challenges facing UAVs in accurately monitoring small deformations at the basin’s edge, we propose a method for inverting the expected parameters of surface subsidence by synergistically integrating InSAR and UAV monitoring. We determined the cumulative subsidence of monitoring points along the dip and strike observation line of the Banji 110,801 working face between 10 April 2021 and 28 June 2022, employing D-InSAR and UAV techniques. By leveraging the complementary strengths of both monitoring techniques, we fused the two types of monitoring data and verified the error of the fusion data to be within 10 cm through leveling data verification. Simulation experiments utilizing the probability integration method and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) optimization algorithm confirmed that the 10 cm data source error remains within the required limits for probability integration parameter inversion. Finally, the BFGS algorithm was employed to invert the parameters of the probability integration method based on the fusion data results. Subsequently, these inversion parameters were used to predict the subsidence at the monitoring point and were compared with the level measured data. The results demonstrate that the use of collaborative InSAR and UAV monitoring technology for inverting the expected parameters of surface subsidence in the mining area yields superior results, aligning with the actual patterns of ground surface movement and deformation. This study addresses the global need for unmanned monitoring of mining-related subsidence. It employs InSAR and UAV technologies in a synergistic approach to monitor surface subsidence in mining regions. This approach harnesses the strengths of multiple data sources and presents a novel concept for the unmanned monitoring of surface subsidence in mining areas, contributing to environmental protection efforts.

Surface Subsidence Monitoring of Mining Areas in Hunan Province Based on Sentinel-1A and DS-InSAR.

Monitoring the surface subsidence in mining areas is conducive to the prevention and control of geological disasters, and the prediction and early warning of accidents. Hunan Province is located in South China. The mineral resource reserves are abundant; however, large and medium-sized mines account for a low proportion of the total, and the concentration of mineral resource distribution is low, meaning that traditional mining monitoring struggles to meet the needs of large-scale monitoring of mining areas in the province. The advantages of Interferometric Synthetic Aperture Radar (InSAR) technology in large-scale deformation monitoring were applied to identify and monitor the surface subsidence of coal mining fields in Hunan Province based on a Sentinel-1A dataset of 86 images taken from 2018 to 2020, and the process of developing surface subsidence was inverted by selecting typical mining areas. The results show that there are 14 places of surface subsidence in the study area, and accidents have occurred in 2 mining areas. In addition, the railway passing through the mining area of Zhouyuan Mountain is affected by the surface subsidence, presenting a potential safety hazard.

Study of the Overlying Strata Movement Law for Paste-Filling Longwall Fully Mechanized in Gaohe Coal Mine

Green mining plays a vital role in achieving environmentally friendly and ecologically sound mining practices. In domestic mining areas, the coal mining method is gradually transitioning from collapse mining to filling mining. Paste filling has been proven effective in controlling surface deformation, although the understanding of its underlying control mechanisms remains incomplete. This study focuses on the E1302 paste-filling working face at Shanxi Gaohe Energy Co., Ltd. and conducts a comprehensive investigation into the movement patterns of overlying strata in longwall fully mechanized mining with paste filling. Through mathematical analysis, a mechanical model for overburden movement in paste-filling faces is established, and the movement behavior of overburden is studied through numerical simulations. Field measurements are conducted to analyze the primary influencing factors of overburden movement, while surface subsidence monitoring is employed to analyze the subsidence characteristics of paste-filling faces. The research reveals that the deflection formula for the roof behind the paste-filling face follows a unitary quartic equation. The key factors influencing significant roof subsidence in filling faces include the filling step distance, filling body strength, and filling rate. Compared to traditional caving mining, filling mining exhibits reduced stress concentration, a smaller range of stress influence, and less deformation in the surrounding rock. The coefficient of gentle subsidence for the overlying rock in filling mining is approximately one-tenth of that in caving mining. The development of cracks in filling mining can be divided into three stages: initial crack propagation, crack recompaction, and stable maintenance of cracks. Notably, the progression of advanced cracks assumes a “sail-shaped” pattern, and the area of crack recompaction is located above the rear side of the excavation. Cracks behind the working face only appear in the basal roof rock layer. When the filling rate in longwall fully mechanized mining with paste filling exceeds 94%, the top plate of the filling working face remains intact but exhibits bending and sinking. The sinking of the top plate increases exponentially with the filling step distance, and approximately 80% of the filling body’s deformation occurs within 20 m after filling. Following backfilling mining, the stability period of the overlying rock is significantly shortened compared to caving mining, resulting in a relatively gentle movement without an active surface movement phase. After six months of backfilling, the overlying rock settles steadily and consistently. The subsidence coefficient for backfilling mining is 0.065, with a maximum surface subsidence of 215 mm. These findings highlight the successful control of surface subsidence. The research outcomes provide an effective theoretical foundation and research direction for predicting overburden movement and surface subsidence in paste-filling faces.

A new sequential homogeneous pixel selection algorithm for distributed scatterer InSAR

ABSTRACT Distributed scatterer interferometric synthetic aperture radar (DS InSAR) technology has been widely used in various fields. Homogeneous pixel selection is a crucial step in the use of DS InSAR, directly affecting the estimation precision and reliability of subsequent parameter calculations. The existing algorithms for selecting homogeneous pixels have inherent limitations, such as requiring many heterogeneous samples and strict requirements surrounding the required number of synthetic aperture radar (SAR) images. To address these problems, a new sequential selection algorithm for homogeneous pixels is proposed, based on the Baumgartner – Weiss–Schindler (BWS) test algorithm and dynamic interval estimation (DIE) theory. According to Monte Carlo simulation experiments, the average standard deviation (STD) of the mean of the rejection of the BWS-DIE algorithm under six sample conditions is 0.014. Compared with three existing algorithms, including the Kolmogorov‒Smirnov (KS), BWS and fast statistically homogeneous pixel selection (FaSHPS) algorithms, the BWS-DIE algorithm improves homogeneous pixel selection precision by 64.3%, 69.4% and 25.3%, respectively. In the real data experiment, 12 scenes of Advanced Land Observing Satellite-1 Phased Array type L-band Synthetic Aperture Radar (ALOS-1 PALSAR) data from February 2007 to March 2011 were used and the BWS-DIE multitemporal InSAR (MT InSAR) method based on the BWS-DIE algorithm was applied to surface subsidence monitoring in the western mining area of Xuzhou, Jiangsu Province, China. The experimental results show that, compared with the Stanford Method for Persistent Scatterers (StaMPS), the BWS-DIE MT InSAR method improves the ability to monitor the maximum subsidence by 12.3%, increases the point density by 5.7 times and decreases the root mean square error (RMSE) by 50%. In addition, new surface deformation patterns are found in the spatial-temporal evolution. The above experimental results show that the proposed BWS-DIE algorithm exhibits remarkable advantages in selection power and selection precision and is not limited by the number of SAR images. The proposed algorithm can further broaden the application scenarios for DS InSAR and provide high-quality and reliable monitoring data for subsequent scientific research.

Monitoring Mining Surface Subsidence with Multi-Temporal Three-Dimensional Unmanned Aerial Vehicle Point Cloud

Long-term and high-intensity coal mining has led to the increasingly serious surface subsidence and environmental problems. Surface subsidence monitoring plays an important role in protecting the ecological environment of the mining area and the sustainable development of modern coal mines. The development of surveying technology has promoted the acquisition of high-resolution terrain data. The combination of an unmanned aerial vehicle (UAV) point cloud and the structure from motion (SfM) method has shown the potential of collecting multi-temporal high-resolution terrain data in complex or inaccessible environments. The difference of the DEM (DoD) is the main method to obtain the surface subsidence in mining areas. However, the obtained digital elevation model (DEM) needs to interpolate the point cloud into the grid, and this process may introduce errors in complex natural topographic environments. Therefore, a complete three-dimensional change analysis is required to quantify the surface change in complex natural terrain. In this study, we propose a quantitative analysis method of ground subsidence based on three-dimensional point cloud. Firstly, the Monte Carlo simulation statistical analysis was adopted to indirectly evaluate the performance of direct georeferencing photogrammetric products. After that, the operation of co-registration was carried out to register the multi-temporal UAV dense matching point cloud. Finally, the model-to-model cloud comparison (M3C2) algorithm was used to quantify the surface change and reveal the spatio-temporal characteristics of surface subsidence. In order to evaluate the proposed method, four periods of multi-temporal UAV photogrammetric data and a period of airborne LiDAR point cloud data were collected in the Yangquan mining area, China, from 2020 to 2022. The 3D precision map of a sparse point cloud generated by Monte Carlo simulation shows that the average precision in X, Y and Z directions is 44.80 mm, 45.22 and 63.60 mm, respectively. The standard deviation range of the M3C2 distance calculated by multi-temporal data in the stable area is 0.13–0.19, indicating the consistency of multi-temporal photogrammetric data of UAV. Compared with DoD, the dynamic moving basin obtained by the M3C2 algorithm based on the 3D point cloud obtained more real surface deformation distribution. This method has high potential in monitoring terrain change in remote areas, and can provide a reference for monitoring similar objects such as landslides.

Surface Subsidence Monitoring in Kunming City with Time-Series InSAR and GNSS

Kunming city is located in the middle of Yunnan Province. Due to large-scale groundwater exploitation and urban development in recent years, this area has been affected by surface subsidence. In this paper, Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) data are used to monitor the surface subsidence in Kunming city area for better analysis and understanding. The study used data of Sentinel-1A from 2018 to 2020 with atmospheric correction based on GACOS to calculate the average annual subsidence rate in Kunming city area, and the results show that the maximum subsidence rate is 48 mm/year. The subsidence obtained by InSAR is compared with the vertical deformation information obtained by eight GNSS stations in continuous operation in the study area. The subsidence rate trend show by the two methods is consistent, which further verifies the validity of InSAR data to reflect the local deformation. Experimental results shown that the eastern and northeastern Dianchi lake areas were affected by underground resources mining, and the induced surface subsidence characteristics were obvious, with the surface subsidence rate reachde 48 mm/year and 37 mm/year respectively. The Kunyang Phosphate Mine also had different degrees of mining subsidence disaster, with the maximum subsidence rate reached 36 mm/year. The subsidence rate of InSAR and GNSS has the same trend on the whole. However, GNSS sites are generally located in stable areas, the settlement amount obtained in the same time period is somewhat different from that of InSAR.

Mining Subsidence Monitoring Based on InSAR Method Fusing Multi-threshold Target

In view of the limitations of traditional InSAR technology in selecting stable target point for orbit refining and surface subsidence inversion in complicated mining area, this paper proposes a time-series InSAR mining area subsidence monitoring method based on the fusion of multi threshold targets. On the basis of the traditional technology, the deviation threshold parameters, the regional window threshold parameters and the coherence threshold parameters are set to extract the relatively stable target points on the ground. Applying this method and traditional InSAR method to practical cases, the monitoring results of surface subsidence in the study area are obtained and verified. The results show that: (1) there are three mining subsidence areas in the mining area, the maximum annual average subsidence rate is -156 mm/a, and the maximum subsidence is -376 mm. Compared with the optical image data, the location of the mining subsidence area is consistent with the mining work area of the coal mine; (2) The absolute average difference of subsidence in the mining area using the two methods shall not exceed 12 mm. It shows that the InSAR method of fusing multi threshold targets can not only effectively overcome the limitations of traditional InSAR, but also ensure high accuracy, and has more advantages in the monitoring of surface subsidence in mining areas.

Ground point extraction using self-adaptive-grid and point to surface comparison

Extracting accurate ground points by using laser point clouds at the complex terrain of the mining area and the dense vegetation environment is a challenging problem. To address this problem, this paper proposes a combined filtering method based on the Self-Adaptive-Grid method (SAG) and Point to Surface Comparison (PSC). First, the adaptive grid is used to extract initial ground seed points, and then, based on the seed points, a Triangulated Irregular Network (TIN) is constructed, and the distance from the reference surface and TIN is computed. These steps are repeated, In each loop, the non-ground points that are significantly far away from the reference surface will be culled, until the ground point is close to the real terrain, where the initial grid size and the distance from the point to the reference surface are gradually reduced according to the gradient. The proposed algorithm overcomes the difficulty of extracting bare ground due to terrain fluctuations. The ground points are effectively separated from the non-ground points in the laser lidar scan data, and the extracted high-precision ground points can be used as important basic data in the terrain modeling of the mining area, surface subsidence monitoring, and research on the laws of surface subsidence, which has great value in popularization and application.

The Stability Analysis Method of Leveling Datum Points in Mining Areas of Western China Based on SBAS-InSAR Technology

The Stability analysis of datum points is a fundamental procedure to verify the monitoring results of leveling settlement. Traditional methods tend to be the employment of repeated monitoring or the increase in measurement points under the same monitoring technique, which are costly and complicated in data processing, making them difficult to be applied in common ground subsidence monitoring scenarios. In view of this, inspired by the idea of external validation, this paper proposes an evaluation method of the stability of datum points in leveling Observation based on Small Baseline Subset InSAR (SBAS-InSAR) technology. Firstly, the paper indicates that differences of subsidence measured by SBAS-InSAR and ground leveling of the same location respectively within close proximity are primarily composed of error caused by unconformity of the position, observation time and measurement method error as well as the displacement of datum points. Based on that, the formulas of calculating errors of unconformity of observation time and the displacement of datum points are derived separately. Afterwards, taking the monitoring of surface subsidence caused by the mining of two working faces in Yingpanhao Coal Mine located in northern Ordos Basin, as practical applications, the feasibility of the method proposed in the paper is verified. And the scope of application of the proposed method is also suggested. This study provides an efficient and economical solution for evaluating the stability of datum points in leveling settlement monitoring, promoting the joint combination of SBAS-InSAR technology and traditional leveling when applied to surface subsidence monitoring.

Time series monitoring and prediction of coal mining subsidence based on multitemporal InSAR technology and GSM-HW model

The surface subsidence caused by coal mining will cause serious environmental problems, and an effective monitoring and prediction system is indispensable. Aiming at this phenomenon, a mining subsidence monitoring and dynamic prediction model combining time series stacking differential interferometric synthetic aperture radar (TSS-DInSAR) technology, small baseline subset InSAR technology (SBAS-InSAR), and golden section method-Holt–Winters (GSM-HW) model was proposed. First, based on the nine scene images of Sentinel-1A satellite in the study area, the time series subsidence of 811 work-face in Guobei coal mine from April 5 to July 10, 2021, was obtained using TSS-DInSAR and SBAS-InSAR monitoring technologies, respectively. Then, the original training samples of the GSM-HW model were generated by combining the cumulative surface subsidence results monitored by TSS-DInSAR and SBAS-InSAR, and the monitoring and prediction of surface subsidence were realized using this model. The experimental results show that the application of TSS-DInSAR and SBAS-InSAR monitoring technology to the impact of surface mining in mining areas has certain reliability, and the GSM-HW prediction model can make up for the deficiency of a single HW model in parameter optimization. The maximum fitting accuracy and prediction accuracy of the model for the 10 monitoring pilot sites are 96.9% and 98.4%, respectively, which can provide a reference for the design of surface monitoring and prediction system in the mining area.

Improved mining subsidence prediction model for high water level area using machine learning and chaos theory

Ground surface monitoring (GSM) points collect information for mining surface subsidence monitoring and environmental governance. However, GSM points submerge in high groundwater mining areas, preventing the collection of monitoring data. The application of machine learning (ML) algorithms to subsidence prediction ignores the uncertainty and irregularity in subsidence changes. Thus, an innovative GSM point information prediction model, which improves the multikernel support vector machine (GA-MK-SVM) using chaos residual theory commonly used for capturing GSM point information, is proposed. The mean relative errors (MREs) between the predicted and observed results of GA-SVM and GA-MK-SVM were 8.2% and 6.1% during active periods, respectively. The GA-MK-SVM also performed better than the GA-SVM during stable periods. The residual error accumulates as the ML algorithms progress, resulting in imprecise predictions of the GSM points. Thus, the GA-MK-SVM model was improved using chaotic theory (Chaos-GA-MK-SVM), with MREs of 5.0% and 0.9% during the active and stable periods, respectively. The accuracy of the proposed model was improved by 1.1% and 3.2% compared with the unimproved GA-MK-SVM, respectively. The proposed approach provides practical GSM point information for mining subsidence studies and governance in high groundwater mines.

Remote sensing-based deformation monitoring of pagodas at the Bagan cultural heritage site, Myanmar

ABSTRACT As a World Cultural Heritage site with sacred landscape featuring an exceptional range of Buddhist art and architecture, much attention has been focused on the sustainable development of Bagan (Myanmar). Particularly, the monitoring of landscape surface subsidence and monument instability is of great importance to the protection and development planning of the Bagan heritage site. In this study, we applied high resolution TerraSAR-X imagery acquired from 2019 to 2020 for deformation monitoring based on the small baseline subset (SBAS) and persistent scatterer synthetic aperture radar interferometry (PSInSAR) approaches. We identified several hotspots and pagodas with displacement anomalies linked to land cover change and previous earthquakes. The cross comparison between SBAS and PSInSAR and the precision of height estimates derived by PSInSAR indicated a millimetric precision of the derived deformation products. The combination of the two multi-temporal SAR interferometry approaches satisfies the two-scale monitoring requirements from landscapes to monuments, particularly for large-scale World Heritage sites. The non-contact monitoring method has potential when traditional methods using field accessibility and surveillance are constrained.

Unmanned Aerial Vehicle Surveying and Mapping Trajectory Scheduling and Autonomous Control for Landslide Monitoring

Real-time and efficient monitoring of geological disasters has received extensive attention in the application of UAV surveying and mapping control technology. The application of traditional landslide monitoring methods lacks the accuracy of control algorithms, which has become a hot issue currently facing. Based on the landslide surface subsidence monitoring method, this article designs the UAV trajectory scheduling subsidence monitoring software, which can monitor the UAV’s flight status and navigation information, and draw the flight trajectory in real time. At the same time, the model solves the problem of storage and management of landslide inspection results by the landslide inspection management system, and realizes the functions of entering and querying landslide information, viewing inspection results, landslide safety judgment, generating reports, and autonomous control. The simulation results show that the global accuracy reaches 0.975, and the algorithm recognition degree reaches 99.8%, which promotes the reliability of the landslide monitoring data for the identification of the surveying and mapping trajectory, and provides a decision-making basis for landslide treatment.

Study on Overlying Strata Movement and Surface Subsidence of Coal Workfaces with Karst Aquifer Water

The overlying strata layers of coal workfaces with karst aquifer water normally causes serious safety problems due to the precipitation, drainage and water inrush, such as a wide range and long term of surface subsidence. In this study, by taking 10,301 working faces of the Daojiao coal mine in Guizhou Province as the engineering background, the numerical model of water-bearing strata with fluid-solid coupling was established by using UDEC to illustrate the laws of overlying strata movement and surface subsidence. A theory model was proposed to calculate the surface settlement caused by the drainage of aquifer based on the principle of effective stress modified by the Biot coefficient αb. The results showed that the corresponding maximum value (0.72 m) and the range of the surface subsidence with the occurrence of karst aquifer water were larger than that of the overlying strata without karst aquifer water (e.g., the maximum value of surface subsidence with 0.1 m). Moreover, the surface subsidence caused by the drainage of aquifer accounted for 17.8% of the total surface subsidence caused by coal mining. According to the field monitoring of surface subsidence in 10,301 working faces, the maximum value was 0.74 m, which was highly consistent with the results of numerical simulation and theoretical analysis. It verified the accuracy and reliability of the numerical model and the theory model in this study.

Surface subsidence monitoring with an improved distributed scatterer interferometric SAR time series method in a filling mining area

Statistically homogeneous pixel (SHP) selection and DS phase optimization are two critical steps in the distributed scatterer interferometric SAR (DS-InSAR) time series method. In this paper, a new algorithm named dynamic hypothesis test of confidence interval (D-HTCI) is proposed, which reduces the wrong selection rate of SHP and increases the number of SHP selections. Using adaptive spatial nonlocal filtering method for DS phase optimization, the phase standard deviation (PSD) and the sum of phase differences (SPD) show that compared with the traditional covariance matrix decomposition method, the optimization quality is improved by 2.1 and 1.8 times, respectively. Combining 24 scenes Sentinel-1A data from September 17, 2017 to July 14, 2018, the method is applied to monitor surface subsidence of the Daizhuang filling mining area (Jining, Shandong, China). The results show that the proposed method has mm-level accuracy for monitoring of surface subsidence in a filling mining area.

Monitoring, analyzing and predicting urban surface subsidence: A case study of Wuhan City, China

Wuhan, one of China's megacities with rapid development, is facing serious surface subsidence. In this study, we combined MT-InSAR, geo-detector, and LSTM (Long Short-Term Memory) to achieve the monitoring, analysis, and prediction of surface subsidence in the main urban districts of Wuhan. The effectiveness of MT-InSAR in monitoring surface subsidence was validated against leveling results. During the monitoring period, the maximum subsidence velocity and uplift velocity were −53.3 mm/year and 18.0 mm/year, respectively. We identified six subsidence regions and explored their deformation characteristics. Further, we analyzed the relationship between the surface subsidence and influencing factors using the geo-detector in a quantitative manner. Our study revealed that the distance to soft soils had the greatest explanatory power on the subsidence. However, we also confirmed that subsidence was affected via coupling effects from multiple factors, suggesting a complex reinforcing relationship among influencing factors. The interaction between the distance to soft soils and the distance to karst collapse prone areas had the largest joint explanatory power on subsidence. Further, we constructed a data-driven LSTM model to predict and analyze the subsidence. The results showed that the LSTM model achieved great performance and presented strong universality, suggesting that it can be used for subsidence prediction in large geographic areas.

DInSAR Monitoring of Surface Subsidence by Fusing Sentinel-1A and -1B Data to Improve Time Resolution in a Mining Area

Monitoring large gradient ground deformation due to temporal and spatial image decoherence has long been a challenge. We attempted an improvement using Sentinel-1A and Sentinel-1B C-band data fusion methods based on the variation law of subsidence velocity of ground leveling monitoring points. This approach improved the temporal resolution from 12 to 6 days. Using a mine in Datong, Shanxi Province, China as an example, 13 scenes of Sentinel-1A data and 12 scenes of Sentinel-1B data were fused and compared with ground-measured data. The results obtained were closer to the measured values than those obtained by single data set (Sentinel-1A or -1B only). Simultaneously, 61 scenes of Sentinel-1A data and 12 scenes of Sentinel-1B data were used to calculate the subsidence of the mining area over two years. The subsidence map was consistent with the actual leveling trend, which reflected the dynamic change of surface subsidence range in the mining area. This study provides an approach using DInSAR technology to monitor large gradient deformation in mining areas and provides an effective method to monitor of surface dynamic deformation.

Surface subsidence monitoring in mining area of SBAS-InSAR based on Sentinel-1A

In this study, the surface subsidence caused by underground mining is monitored by the technique of SBAS-InSAR. The second west mining area of Longshou mine, Jinchang Jinchuan Cu-Ni sulfide mine, Gansu province, is selected as the research area for the experiment. The 29 scene elevation rail Sentinel-1A image data from 2019.03-2020.3 are analyzed for the sequence analysis of the SBAS-InSAR. The vertical subsidence rate and cumulative subsidence results of the surface sequence of the mining area are obtained. According to the study, the minimum error of cumulative sedimentation between SBAS-InSAR and D-InSAR is 0.1mm, the maximum error is 21.37mm, and the median error is ±6.65mm. The minimum error of cumulative settlement between SBAS-InSAR and levelling is 0.66mm, the maximum is 23.74mm, and the median error is ±10.79mm. The results show that the application of the SBAS-InSAR technique in the monitoring of surface subsidence in the mining area is reliable, which provides an important basis for the subsequent safe mining in the mining area.