Surface Subsidence Analysis and Prediction in an Open-Pit Mine Using Time-Series InSAR and a CL-TSF Hybrid Model

Abstract

Surface subsidence in mining areas is a widespread, long-term, and slow-onset geological hazard that can induce cascading failures, including slope instability and ground collapse, thereby threatening infrastructure and human safety. Accurate characterization of its spatiotemporal evolution and reliable prediction of future trends are therefore essential for effective mine safety management and hazard assessment. This study investigates an open-pit mine by integrating time-series Interferometric Synthetic Aperture Radar (InSAR) monitoring with a deep learning–based prediction framework. A total of 70 Sentinel-1A synthetic aperture radar (SAR) images acquired between January 2023 and May 2025 were processed to quantify surface deformation. The results reveal a large-scale subsidence funnel, with a maximum subsidence rate of 143.00 mm/yr and a cumulative displacement of −337.89 mm. The observed deformation is controlled by combined effects of rainfall, seismic activity, and local geological conditions. To predict the temporal evolution of subsidence, a hybrid convolutional neural network–long short-term memory (CNN–LSTM) time-series forecasting model (CL-TSF) is proposed. By integrating convolutional feature extraction with long short-term memory–based sequence modeling, the model effectively captures spatial patterns and long-term temporal dependencies. Compared with conventional CNN and LSTM models, the proposed approach achieves superior performance, with a Mean Absolute Percentage Error (MAPE) of 2.24% and a Root Mean Square Error (RMSE) of 5.260. Its robustness is validated through accurate multi-step prediction of the final five deformation periods. These findings provide insights into mining-induced subsidence mechanisms and demonstrate the potential of the proposed framework for dynamic early warning and risk assessment in mining areas.