Predicting surface subsidence in large-scale mining areas based on time-series InSAR and the IRIME-LSTM model
CEHN Lanlan1(), FAN Yongchao2, XIAO Haiping3(), WAN Junhui3, CHEN Lei3
1. School of Resources and Civil Engineering, Gannan University of Science and Technology, Ganzhou 341000, China 2. Jiangxi Piaotang Tungsten Industry Co., Ltd., Ganzhou 341500, China 3. School of Civil Engineering and Surveying and Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
Interferometric synthetic aperture Radar (InSAR) technology serves as a significant approach for analyzing surface subsidence in large-scale mining areas. Accurately predicting surface subsidence plays a significant role in preventing geological disasters. The long short-term memory (LSTM) network model faces challenges in parameter selection, while the rime optimization algorithm (RIME) is susceptible to local optimum and dependence on the initial solution. Considering these challenges, this study proposed a surface subsidence prediction model with LSTM optimized by the improved rime optimization algorithm (IRIME). The IRIME incorporated chaotic mapping, the improved Lévy flight mechanism, and the global exploration strategy of the hunter-prey optimizer (HPO). The proposed model is also referred to as the IRIME-LSTM model. With the Honghui coal mine as the study area, this study obtained the subsidence time series of highly coherent points in the mining area using small baseline subset (SBAS)-InSAR technology. Subsequently, this study conducted multi-step predictions of these highly coherent points using the IRIME-LSTM model, with the prediction results compared with the InSAR monitoring data. The results of this study indicate that the IRIME-LSTM model yielded a root mean square error (RMSE) of 2.65 mm, a mean absolute error (MAE) of 1.59 mm, and a mean absolute percentage error (MAPE) of 3.92 % in the overall test set. Compared to the RIME-LSTM and GS-LSTM models, the IRIME-LSTM model reduced the RMSE by 37.20 % and 51.73 %, the MAE by 42.60 % and 56.32 %, and the MAPE by 35.63 % and 50.51 %, respectively, demonstrating its high reliability and feasibility.
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CEHN Lanlan, FAN Yongchao, XIAO Haiping, WAN Junhui, CHEN Lei. Predicting surface subsidence in large-scale mining areas based on time-series InSAR and the IRIME-LSTM model. Remote Sensing for Natural Resources, 2025, 37(3): 245-252.
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2025, Vol. 37 
), 范永超2, 肖海平3(
