High-precision monitoring and prediction of mining area surface subsidence using SBAS-InSAR and CNN-BiGRU-attention model - Mingfei Zhu; Xuexiang Yu; Hao Tan; Jiajia Yuan; Kai Chen; Shicheng Xie; Yuchen Han; Wenjiang Long (Scientific Reports)
Abstract Coal mining-induced surface subsidence can significantly impact resident safety and hinder regional sustainable development, making precise subsidence monitoring and prediction critical. Existing mining subsidence monitoring technologies often exhibit low spatiotemporal resolution, while subsidence prediction models suffer from heavy dependence on data quality and model assumptions, as well as imprecise parameters. This study addresses these limitations by proposing a novel mining subsidence monitoring and prediction method based on Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the Convolutional Neural Network—Bidirectional Gated Recurrent Unit—Attention (CNN-BiGRU-Attention) model. Focusing on the Banji mining area in Anhui Province, ground subsidence was monitored from July 15, 2021, to September 3, 2023, utilizing SBAS-InSAR technology with Sentinel-1A satellite data. The monitoring results were validated using leveling measurement data. A CNN-BiGRU-Attention prediction model was subsequently constructed based on the time-series monitoring data. The results indicate that the surface subsidence rate in the study area decreases progressively from northwest to southeast, with an average subsidence rate ranging from -49.844 mm/year to -14.810 mm/year. At feature points, the CNN-BiGRU-Attention model effectively captures the characteristics of subsidence time-series changes. For regional subsidence prediction, this model maintains the smallest error, with Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values of 1.27 mm and 1.44 mm, respectively, and an absolute prediction error of less than 1 mm in most areas. This study integrates SBAS-InSAR technology with the CNN-BiGRU-Attention model to enable unmanned monitoring and prediction of mining subsidence. In comparison to traditional methods, this approach not only reduces monitoring costs but also enhances the accuracy of subsidence predictions, offering critical technical support for the sustainable development of mining areas.