Multi-source approach research on prediction and mechanism of mountain surface subsidence caused by underground mining

Mining activities may trigger hazards such as mountain subsidence. To predict the extent of mountain subsidence and analyze the evolutionary characteristics of mountain subsidence, the optical images and Small Baseline Subset InSAR (SBAS-InSAR) method were used, and the mountain subsidence was circled and interpreted. The cumulative subsidence in the area from 2024 to 2026 was predicted by combining the long short-term memory (LSTM) method, and the mountain surface subsidence slip was derived using MatDEM. The results of the study show that mountain surface subsidence begins with the formation of a primary subsidence zone, which slowly leads to the formation of primary and secondary subsidence zones. Under the influence of the penetrating channel, the primary and secondary subsidence areas merge to form a larger subsidence area. The subsidence area gradually disintegrates into several small areas during the sliding process, and the small areas underneath contribute to the main force of the subsidence movement, with a substantial slip displacement. Based on this study, it is concluded that the accuracy of the results obtained from the LSTM method is higher than that of the numerical simulation results, and the maximum cumulative subsidence is expected to reach 2,180 mm in 2026.