SBAS-InSAR calculation result

Mining subsidence is a pervasive geohazard in coal basins, and precise, reliable deformation monitoring is essential for effective risk mitigation. Conventional time series InSAR suffers from vegetation induced decorrelation, bare earth scattering, and atmospheric delays, which reduce coherent pixels; moreover, most predictive models leverage only temporal information. To address these limitations, we introduce an integrated DS InSAR + CNN LSTM framework for subsidence monitoring and forecasting. Forty three Sentinel 1A scenes (2017–2018), corrected with 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.