The Sichuan–Tibet Railway (STR) is currently under construction and serves as an important transportation route in western China. Identifying potential geohazards along the route is important for project construction. However, research on the frozen soil of the Western Sichuan Plateau, and on frozen soil identification using interferometric synthetic aperture radar (InSAR) is relatively negligible. As a low-cost, all-weather spatial geodesy tool, InSAR is frequently used for geohazard identification. We selected a study area located along the Litang section of the STR, starting from Litang County in the east and extending 60 km to the west. The geological conditions along the line are complex, with numerous fault zones and hidden danger points for landslide. To identify unstable slopes along the line, distribute scatterer InSAR (DS-InSAR) was used to obtain surface displacement information from 2018 to 2021. Based on the displacement information obtained from the ascending and descending orbit images from Sentinel-1, a spatial density clustering method identified 377 and 388 unstable slopes in the study area, respectively, of these, 132 were consistent. The identified unstable slopes were mostly located in areas with a relatively high altitude and moderate slope. The Luanshibao landslide, which is a typical landslide in the study area, had notable signs of displacement, where the displacement rate along the back edge of the landslide can reach 20 mm/a. An inversion method for the seasonal frozen soil area distribution was proposed based on the periodic subsidence and uplift model and time-series monitoring data; the calculated seasonal freeze–thaw amplitude exceeded 20 mm. Further analysis revealed a 2-month lag in the response of the freeze–thaw phenomenon to the air temperature. This study demonstrated that DS-InSAR offers optimal surface displacement data, which can provide an important basis to identify engineering geological hazards.

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.

Predicting the height of the water-conducting fracture zone (WCFZ) is crucial for preventing water inrush and ensuring safe underground mining operations. In this study, we propose a novel model combining CatBoost, XGBoost, and AdaBoost with SSA, HHO, and LEA. Key stratum data (DK, TK) and dip mining intensity data were integrated into the existing parameters for WCFZ height prediction. The main influence angle tangent, derived from the probability integral method, replaces the hard rock ratio coefficient. A total of 104 field datasets with eight input parameters were used, with WCFZ height as the dependent variable. The model was validated using five-fold cross-validation and evaluated with root mean square error ($RMSE$), mean absolute error ($MAE$), $R^2$, and mean relative error ($MRE$). The Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) was applied to rank the models. The CAT-HHO model demonstrated the best performance. Using this model, predictions of WCFZ height under varying dip mining intensities showed an approximately linear relationship. SHAP analysis identified mining thickness as the most influential factor. Removing key stratum data from models significantly reduced prediction accuracy. The results highlight the model’s ability to improve WCFZ height prediction, offering insights for water inrush prevention in coal mining operations and providing guidance for applying machine learning to similar challenges.