矿区地表形变监测及预测方法研究

利用2018年1月至2021年12月的47景Sentinel-1A影像，基于SBAS-InSAR技术，对窑街矿区进行沉降监测。根据矿区沉降特征，构建GM(2,1)、BP神经网络、PSO-SVR和LSTM 4种预测模型，对3个矿区2020—2021年形变中心沉降值进行预测分析。结果显示：窑街矿区大部分区域处于稳定状态，矿区中心形成明显的沉降漏斗，最大平均沉降速率为157.01 mm/a, 2018—2021年最大累积沉降量达到681.82 mm,存在发生采空区大面积塌陷和次生地质灾害的危险。预测结果显示，4种模型表现出不同的预测精度，LSTM模型预测精度最高，可作为矿区地表沉降相对可靠的预测模型。

Research on Monitoring and Forecasting Methods of Surface Deformation in Mining Area

Based on SBAS-InSAR technology, 47 sentinel-1A images from January 2018 to December 2021 were used to monitor the subsidence of Yaojie mining area. According to the subsidence characteristics of mining areas, 4 prediction models, GM(2,1), BP neural network, PSO-SVR and LSTM, are constructed to predict and analyze the subsidence values of deformation centers in three mining areas from 2018 to 2021. The results show that most areas of Yaojie Mining Area are in a stable state, and the obvious subsidence funnel is formed in the center of the mining area. The maximum annual average subsidence rate is 157.01 mm/a, and the maximum accumulated subsidence from 2020 to 2021 is 681.82 mm, so there is a danger of large-scale subsidence and secondary geological disasters in mined-out areas. The prediction results show that the four models show different prediction accuracy, and the LSTM model has the highest prediction accuracy, which can be used as a relatively reliable prediction model of land subsidence in mining areas.