Research on Mining Area Surface Subsidence Based on Time-Series InSAR Images and Deep Learning Models

Surface subsidence in mining areas is characterized by extensive spatial coverage, complex evolutionary processes, and diverse influencing factors. Conventional monitoring methods and prediction models can hardly meet the demands of large-area continuous monitoring and accurate prediction. Based on time-series InSAR observations and deep learning models, this study investigates the temporal evolution of surface subsidence in mining areas and evaluates the predictive accuracy of the proposed deep learning model. A total of 71 ascending-track Sentinel-1A single-look complex (SLC) images acquired over an open-pit mining area from 9 June 2021 to 14 January 2026 were selected. The PS-InSAR inversion is first implemented to extract highly coherent stable scatterers with small deformation values. Taken as ground control points (GCPs), these points are utilized for orbital correction and interferogram re-flattening in SBAS-InSAR processing to achieve PS-point-constrained deformation monitoring. Taking time-series InSAR deformation datasets, local spatial deformation features, and environmental factors including rainfall, digital elevation model (DEM), slope, and aspect as joint model inputs, a multi-source spatiotemporal fusion prediction model GeoSTF-Former is established to achieve pixel-scale prediction of surface subsidence in mining areas. Experimental results show that the spatial continuity and temporal stability of SBAS-InSAR deformation results are effectively improved after adopting PS stable-point constraints. Compared with the unconstrained scheme, the average RMSE and MAE of monitoring results decrease by 31.9% and 35.3%, respectively, while the coefficient of determination R2 increases by 0.10. On the test dataset, the GeoSTF-Former model yields an RMSE of 3.08 mm, an MAE of 2.42 mm, and a coefficient of determination R2 of 0.96 at the pixel scale. These results verify that the combination of PS-stable-point-constrained SBAS-InSAR measurements and multi-source spatiotemporal deep learning models can deliver reliable technical support for surface subsidence monitoring and trend forecasting of mining areas.