Surface Deformation Monitoring and Prediction of InSAR-Hybrid Deep Learning Model for Subsidence Funnels

Mining subsidence is a pervasive geohazard in coal basins, and precise and reliable deformation monitoring is essential to effective risk mitigation. Conventional time-series Interferometric Synthetic Aperture Radar (InSAR) suffers from vegetation-induced decorrelation and atmospheric delays. Most predictive models leverage only temporal information. We introduced an integrated DS InSAR + CNN LSTM framework for subsidence monitoring and forecasting. Forty-three Sentinel-1A scenes (2017–2018), corrected with Generic Atmospheric Correction Online Service for InSAR (GACOS) data, were processed to derive cumulative deformation, cross-validated against multi-view SBAS InSAR, and used to train a CNN LSTM network that predicts trends one year in advance. The findings indicate that (1) DS InSAR provides 2.83 times the monitoring density of SBAS InSAR, with deformation rate R2 = 0.83, RMSE = 0.0028 m/a, and MAE = 0.0019 m/a at common pixels. The RMS average decrease in GACOS atmospheric delay phase correction is 2.52 mm. (2) High- and low-settlement zones comprise 0.11% and 92.32% of the area, respectively; maximum velocity reaches 190.61 mm/a, with a cumulative subsidence of −338.33 mm. (3) Across the five zones with the most severe subsidence, the CNN–LSTM model attains R2 values of 0.97–0.99 and RMSE below 1 mm, markedly outperforming the standalone LSTM network. (4) Deformation correlated strongly with geological structures, groundwater decline (R2 = 0.66–0.78), and precipitation (slope > 0.33), highlighting coupled natural and anthropogenic control.