BTFC-SBAS : A Boltzmann Time Function Constrained SBAS-InSAR Method for Nonlinear Deformation Estimation in Mining Areas

Wuda District of Wuhai City in Inner Mongolia Autonomous Region is rich in mineral resources, with proven coal reserves of 660 million tons. It is of great significance for local economic development and industrial production safety to monitor and study the dynamic settlement of Wuda Mining Area. The small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology has been widely used in surface deformation monitoring for its easy to realize long-term monitoring. In this paper, the 20 scene ascending orbit Sentinel1A images from August 2019 to November 2020 are used. The geographical coordinate of the study area is 39°20’–39°40’N, 106°30’–106°50’E and the total area is 219.716 square kilometers. The displacement results show that the maximum subsidence velocity is about 198 mm/yr. The terrain subsidence mainly occurs in the west of Wuda District, which is mainly mountain areas and mining areas, including Wuhushan coal mine, Huangbaici coal mine and Suhaitu Coal Mine and the surface of the mining areas showed a long-term subsidence trend. The southeastern part of the study area is mainly residential area, and the surface uplift is closely related to human production activities.

This study investigates the influence of fault reactivation on mining-induced surface subsidence, focusing on the 171107 working face of Liuzhuang Coal Mine in Fuyang City, Anhui Province. Using 29 Sentinel-1A ascending track SAR images acquired between January 8 and December 21, 2024. Surface deformation data were obtained through small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) and enhanced SBAS-InSAR (ESBAS-InSAR) techniques. The results show that the ESBAS-InSAR method significantly improves data coverage in low-coherence and high-deformation regions by synergistically inverting persistent scatterers (PS) and distributed scatterers (DS). The ESBAS-InSAR monitoring error increases near the subsidence center but is reduced after Gaussian curve fitting, with cumulative average errors in the azimuth and vertical directions of 15.8 mm and 17.04 mm, respectively. Profile analysis revealed that mining reactivated the F4 normal fault and the F1-2 reverse fault, leading to anomalous deformation zones with stepped and funnel-shaped patterns. This study highlights the importance of fault reactivation in shaping subsidence patterns and provides valuable insights for monitoring and early warning of geological hazards in coal mines.

Ground subsidence in mined-out areas has irreversible impacts on residents’ lives and infrastructure, making its monitoring and prediction crucial for ensuring safety, protecting the ecological environment, and promoting sustainable development. This study employed the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to process Sentinel-1A satellite images of Liaoyuan’s Northern New District from August 2022 to March 2025, deriving ground deformation data. The SBAS-InSAR results were validated using unmanned aerial vehicle (UAV) measurements. Monitoring revealed deformation rates ranging from −26.80 mm/year (subsidence) to 13.12 mm/year (uplift) in the area, with a maximum cumulative subsidence of 59.59 mm observed near the Xi’an Sixth District. Based on spatiotemporal patterns, most mining-induced subsidence in the study area is in its late stage, primarily caused by progressive compaction of fractured rock masses and voids within the collapse and fracture zones. Using subsidence data from August 2022 to March 2024, three prediction models—LSTM, GRU, and TCN-GRU—were trained and subsequently applied to forecast subsidence from March 2024 to August 2025. Comparisons between the predictions and SBAS-InSAR measurements showed that all models achieved high accuracy. Among them, the TCN-GRU model yielded predictions closest to the actual values, with a correlation coefficient exceeding 0.95, validating its potential for application in time-series settlement monitoring.

Tailings storage facility (TSF) is a critical component of the mining industry, serving to safely contain waste generated during mineral extraction. At Chatree Gold Mine, the largest and only active gold mine in Thailand, TSF No.2 handles approximately 5 million tons of tailings annually. Effective monitoring of the facility is crucial for hazard prevention and regulatory compliance. This study aims to enhance the monitoring scheme of TSF No.2 by employing Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique, processed via MintPy software, to analyze ground deformation. Despite traditional monitoring methods, such as in-situ geodetic surveying, offering valuable insights, they may not provide timely warnings of potential instability and are costly when applied to large-scale areas. By conducting a time-series SBAS-InSAR analysis from May 2016 to January 2024 and validating results against in-situ geodetic as well as finite element modelling (FEM) results, this study evaluates the reliability of InSAR for early hazard detection. The results of deformation velocity map revealed deformation rates ranging from -25.2 mm/year to -1.05 mm/year. Three geotechnical risk areas were identified around Survey Points (SP) 04, 05, and 06. The area near SP-05 exhibited the highest subsidence rate at -25.2 mm/year, corroborated by FE modeling, which indicated cumulative displacements up to -209.95 mm at the upstream area. Cross-validation yielded strong correlations (R² = 0.858 with in-situ geodetic data; R² = 0.932 with FE modeling) and root mean square errors (14.50 mm and 7.75 mm, respectively). These results confirm SBAS-InSAR as a reliable tool for monitoring ground deformation in mining environments, supporting early detection of geotechnical hazards and enhancing operational safety.

Given the increasingly serious geological disasters caused by underground mining in the Hancheng mining area in China and the existing problems with mining subsidence prediction models, this article uses the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology to process 109 Sentinel-1A images of this mining area from December 2015 to February 2020. The results show that there are three subsidences: one in Donganshang, one in south of Zhuyuan village, and one in Shandizhaizi village. In the basin, the maximum annual average subsidence rate is 300 mm/a, and the maximum cumulative subsidence is 1000 mm. The SBAS-InSAR results are compared with Global Positioning System (GPS) observation results, and the correlation coefficient is 74%. Finally, a simulated annealing (SA) algorithm is used to estimate the optimal parameters of a support vector regression (SVR) prediction model, which is applied for mining subsidence prediction. The prediction results are compared with the results of SVR and the GM (1, 1). The minimum value of the coefficient of determination for prediction with SA-SVR model is 0.57, which is significantly better than that those of the other two prediction methods. The results indicate that the proposed prediction model offers high subsidence prediction accuracy and fully meets the requirements of engineering applications.

With the increasing availability of satellite monitoring data, the demand for storage and computational resources for updating the results of monitoring the surface subsidence in a mining area continues to rise. Sequential adjustment (SA) models are considered effective for rapidly updating time series interferometry synthetic aperture radar (TS-InSAR) measurements. However, the accuracy of surface subsidence values estimated through traditional sequential adjustment is highly sensitive to abnormal observations or prior information on anomalies. Moreover, the surface subsidence associated with mining exhibits nonlinear and large gradient characteristics, making general InSAR methods challenging for obtaining reliable monitoring results. In this study, we employ the phase unwrapping network (PUNet) to obtain unwrapped values of differential interferograms. To mitigate the impact of abnormal errors in the near real-time small baseline subset InSAR (SBAS-InSAR) sequential updating process in mining areas, a robust sequential adjustment method based on M-estimation is proposed to estimate the temporal deformation parameters by using the equivalent weight model. Using a coal backfilling mining face in Shanxi, China, as the study area and the Sentinel-1 SAR dataset, we comprehensively evaluate the performance of unwrapping methods and subsidence time series estimation techniques and evaluate the effect of filling mining on surface subsidence control. The results are validated using leveling measurements within the study area. The relative error of the proposed method is less than 5%, which can meet the requirements of monitoring the surface subsidence in mining areas. The method proposed in this study not only enhances computational efficiency but also addresses the issue of underestimation encountered by InSAR methods in mining area applications. Furthermore, it also mitigates unwrapping phase anomalies on the monitoring results.

Employing a small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and hotspot analysis, this study identified 81 potential landslides in a 768.7 km2 area of Xiaojin county, eastern Tibetan Plateau. Subsequent time-series deformation analysis revealed that these potential landslides are in the secondary creep stage. The newly identified landslides were compared to a landslide inventory (LI), established through field surveying, in terms of causative factors, including altitude, slope, relief amplitude, distance to river, distance to road, and slope curvature. From the comparison, the InSAR technique showed the following advantages: (1) it identified 25 potential landslides at high altitudes (>3415 m) in addition to the low-altitude landslides identified through the field survey. (2) It obtained approximately 37.5% and 70% increases in the number of potential landslides in the slope angle ranges of 20°–30° and 30°–40°, respectively. (3) It revealed significant increases in potential landslides in every relief amplitude bin, especially in the range from 58 m to 92 m. (4) It can highlight key geological factors controlling landslides, i.e., the stratigraphic occurrence and key joints as the InSAR technique is a powerful tool for identifying landslides in all dip directions. (5) It reveals the dominant failure modes, such as sliding along the soil–rock interface and/or interfaces formed by complicated combinations of discontinuities. This work presents the significant potential of InSAR techniques in gaining deeper knowledge on landslide development in alpine forest regions.

Landslide susceptibility assessment (LSA) is still dominated by static conditioning layers with limited interpretability, and it under-utilises kinematic deformation for operational prioritisation. To address these gaps, we develop a Landslide Optimised Susceptibility Assessment (LOSA) that dynamically fuses Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) time-series deformation with a Random Forest (RF) susceptibility surface via a spatiotemporal weighting matrix, and explains drivers with SHapley Additive exPlanation (SHAP). Using 186 Sentinel-1A scenes (2019–2022) over Longhua County, SBAS-InSAR resolves millimetre-scale motion (peak 104.95 mm/y), while Random-Forest attains Area Under Curve (AUC) = 0.838. The integrated LOSA delivers a 70.66% contraction of the extremely high risk footprint and indicates more targeted mapping under the conjunctive fusion, with field audits confirming hotspot activity. LOSA is a transparent, adaptive, and rapidly updatable tool that links kinematics with interpretable modelling to support landslide-risk governance.

Health monitoring is important for densely distributed urban infrastructures, particularly in cities undergoing rapid economic progress. Permanent scatterer interferometry (PSI) is an advanced remote sensing observation technique that is commonly used in urban infrastructure monitoring. However, the rapid construction of infrastructures may easily cause a loss of coherence for radar interferometry, inducing a low density of effective permanent scatterer (PS) points, which is the main limitation of PSI. In order to address these problems, a novel time-series synthetic aperture radar interferometry (InSAR) process based on the adaptive window homogeneous pixel selection and phase optimization (AWHPSPO) algorithm and thermal expansion linear model (TELM) is proposed. Firstly, for homogeneous point selection, information on both the time-series intensity and deformation phases is considered, which can compensate for the defects of insufficient homogeneous samples and low phase quality in traditional distributed scatterer interferometric synthetic aperture radar (DS-InSAR) processing. Secondly, the physical, thermal expansion component, which reflects the material properties of the infrastructures, is introduced into the traditional linear model, which can more rationally reflect the temporal evolution of deformation variation, and the thermal expansion coefficients can be estimated simultaneously with the deformation parameters. In order to verify our proposed algorithm, the Orange Island area in Changsha City, China, was selected as the study area in this experiment. Three years of its historical time-series deformation fields and thermal expansion coefficients were regenerated. With the use of high-resolution TerraSAR-X radar satellite images, a maximum accumulated settlement of 12.3 mm and a minor uplift of 8.2 mm were detected. Crossvalidation with small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) results using Sentinel 1A data proved the reliability of AWHPSPO. The proposed algorithm can provide a reference for the control of the health and safety of urban infrastructures.

The ancient Suoertou landslide seriously threatens the surrounding population's lives and property. Monitoring and predicting this landslide is crucial to ensure the affected areas’ safety. The previous research on the landslide's displacement characteristics and mechanisms has lacked detailed analyses. Additionally, its future development trends must be understood. Therefore, we conducted a detailed analysis of the ancient Suoertou landslide's displacement characteristics and mechanisms using small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) monitoring results from 2018 to 2023. Furthermore, by applying a gated recurrent unit (GRU) prediction mode that incorporates the refined displacement characteristics and mechanisms, we forecasted the landslide's displacement trends. The results show that this landslide is currently undergoing overall slow displacement with violent fluctuations in localized areas. Tectonic movement, precipitation, human activities, river erosion, and other factors interact, forming a vicious development displacement mechanism. According to our prediction, the displacement of this landslide will be in a trend of fluctuating increase from June 2023 to June 2024. In particular, the local area will undergo abnormal displacement acceleration. The results of the current research provide a scientific basis upon which to monitor landslides, promote their management, and reduce the risk of losses due to landslide disasters.

Landslides are geological events that frequently cause major disasters. Research on landslides is essential, but current studies mostly use historical landslide data and do not reflect dynamic, real-time research results. In this study, landslide deformations and land-use changes were used to analyze the landslide distribution in Fengjie County and Wushan County in the Three Gorges Reservoir Area (TGRA) by using interferometric and polarimetric SAR. In this study, the mean annual rate of landslide deformations was obtained using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) for the ALOS-2 (2014–2019) data. Land-use changes were based on the 2007 and 2017 land-use results from dual-polarization ALOS-1 and ALOS-2 data, respectively. To address the problem of classification accuracy reduction caused by geometric distortion in mountainous areas, we first used texture maps and pseudocolor maps synthesized with dual-polarization intensity maps to perform classification with random forest (RF), and then we used coherence and slope maps to run the K-Means algorithm (KMA). We named this the secondary classification method. It is an improvement on the single classification method, exhibiting a 94% classification accuracy, especially in rugged areas. Combined with land-use changes, GIS spatial analysis was used to analyze the spatial distribution of landslides, and it was found that the landslide rate was significantly correlated with the type after change, with a correlation coefficient of 0.7. In addition, land-use types associated with human activities, such as cultivated vegetation, were more likely to cause landslide deformation, which can be used to guide local land-use planning.

Land subsidence, the superposition of effects from natural environments and human activities, has posed substantial threats to sustainable development in mining areas. Monitoring land subsidence is particularly crucial. However, most monitoring models ignore the combined effects of subsidence‐induced factors, making it difficult to accurately capture the spatiotemporal heterogeneity of subsidence. To address this challenge, this study proposed a GeoDistillBERT‐BiLSTM framework for time‐series land subsidence monitoring by fusing multimodal features. Firstly, subsidence patterns from 2020 to 2022 were assessed through Small Baseline Subset InSAR (SBAS‐InSAR). Then, the Pressure‐State‐Response (PSR) framework was used to screen representative factors influencing subsidence from three aspects: human activities, natural environment, and subsidence velocities. Subsequently, a GeoDistillBERT‐BiLSTM framework was developed that combines a geological modal attention mechanism with a modality‐aware adaptive loss function. It could dynamically assign importance coefficients to different factors by generating spatiotemporal weight matrices, facilitating the fusion of multimodal features for time‐series monitoring, thus revealing the spatiotemporal changes of subsidence. Based on this, Geodetector is introduced to explore mechanisms behind subsidence dynamics. This study used the Dagushan open‐pit mine as a case study; it demonstrated that the GeoDistillBERT‐BiLSTM framework improves the fusion accuracy of multimodal features by establishing spatiotemporal weight matrices. Compared with conventional statistical models, the loss function L total reduced by 2%. Compared with other factors, human activities and their interaction with NDVI played a key role in subsidence mechanisms. This study provides a novel approach that integrates multiple factors and spatiotemporal correlations for time‐series subsidence monitoring, helping to prevent geological hazards in mining areas.

Sea-level rise has important implications for the economic and infrastructure security of coastal cities. Land subsidence further exacerbates relative sea-level rise. The Beijing–Tianjin–Hebei region (BTHR) along the Bohai Bay is one of the areas most severely affected by ground subsidence in the world. This study applies the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS InSAR) method to analyze 47 ALOS PALSAR-2 images with five frames, mapping subsidence across 21,677.7 km2 and revealing spatial patterns and trends over time from 2015 to 2021. This is one of the few published research studies for large-scale and long-term analysis of its kind using ALOS-2 data in this region. The results reveal the existence of six major areas affected by severe subsidence in the study area, with the most pronounced in Jinzhan Town, Beijing, with the maximum subsiding velocity of −94.42 mm/y. Except for the two subsidence areas located in Chaoyang District of Beijing and Guangyang District of Langfang City, the other areas with serious subsidence detected are all located in suburban areas; this means that the strict regulations of controlling urban subsidence for downtown areas in the BTHR have worked. The accumulated subsidence is highly correlated with the time in the time series. Moreover, the subsidence of 161.4 km of the Beijing–Tianjin Inter-City High-Speed Railway (HSR) and 194.5 km of the Beijing–Shanghai HSR (out of a total length of 1318 km) were analyzed. It is the first time that PALSAR-2 data have been used to simultaneously investigate the subsidence along two important HSR lines in China and to analyze relatively long sections of the routes. The above two railways intersect five and seven subsiding areas, respectively. Within the range of the monitored railway line, the percentage of the section with subsidence velocity below −10 mm/y in the monitoring length range is 11.2% and 27.9%; this indicates that the Beijing–Shanghai HSR has suffered more serious subsidence than the Beijing–Tianjin Inter-City HSR within the monitoring period. This research is also beneficial for assessing the subsidence risk associated with different railways. In addition, this study further analyzed the potential reasons for the serious land subsidence of the identified areas. The results of the geological interpretation still indicate that the main cause of subsidence in the area is due to hydrogeological characteristics and underground water withdrawal.

Wuhan city is the biggest city in central China and has suffered subsidence problems in recent years because of its rapid urban construction. However, longtime and wide range monitoring of land subsidence is lacking. The causes of subsidence also require further study, such as natural conditions and human activities. We use small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) method and high-resolution RADARSAT-2 images acquired between 2015 and 2018 to derive subsidence. The SBAS-InSAR results are validated by 56 leveling benchmarks where two readings of elevation were recorded. Two natural factors (carbonate rock and soft soils) and three human factors (groundwater exploitation, subway excavation and urban construction) are investigated for their relationships with land subsidence. Results show that four major areas of subsidence are detected and the subsidence rate varies from −51.56 to 27.80 millimeters per year (mm/yr) with an average of −0.03 mm/yr. More than 83.81% of persistent scattered (PS) points obtain a standard deviation of less than −6 mm/yr, and the difference between SBAS-InSAR method and leveling data is less than 5 mm/yr. Thus, we conclude that SBAS-InSAR method with Radarsat-2 data is reliable for longtime monitoring of land subsidence covering a large area in Wuhan city. In addition, land subsidence is caused by a combination of natural conditions and human activities. Natural conditions provide a basis for subsidence and make subsidence possible. Human activities are driving factors and make subsidence happen. Moreover, subsidence information could be used in disaster prevention, urban planning, and hydrological modeling.

Land subsidence significantly threatens urban infrastructure, agricultural productivity, and environmental sustainability. This study develops a land subsidence susceptibility model by integrating Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) data with key geospatial factors using machine learning approaches. The study focuses on the Attica prefecture, Greece, and utilizes SBAS InSAR data from 2015 to 2021 to extract ground deformation velocities by classifying them into four susceptibility levels: stable, low, moderate, and high. The susceptibility results indicate that stable zones constitute 58.2% of the study area, followed by low (27.2%), moderate (11.2%), and high susceptibility zones (3.4%), predominantly concentrated in areas undergoing hydrological stress and urbanization. Random Forest (RF) and XGBoost (XGB) models incorporate a comprehensive set of causal factors, including slope, aspect, land use, groundwater level, geology, and rainfall. The evaluation of the models includes accuracy metrics and confusion matrices. The XGB model achieved the highest performance, recording an accuracy of 94%, with well-balanced predictions across all susceptibility classes. Addressing class imbalance during model training improved the recall of minority classes, though with slight trade-offs in precision. Feature importance analysis identifies proximity to streams, land use, aspect, rainfall, and groundwater extraction as the most influential factors driving subsidence susceptibility. This methodology demonstrates high reliability and robustness in predicting land subsidence susceptibility, providing critical insights for land-use planning and mitigation strategies. These findings establish a scalable framework for regional and global applications, contributing to sustainable land management and risk reduction efforts.