Dynamic prediction model of mining subsidence combined with D-InSAR technical parameter inversion

It is of great significance to obtain timely and accurate information of surface subsidence caused by mining. The probability integral method (PIM) model is more suitable for mining subsidence prediction in China and has been widely used. However, the PIM model has the question on too fast convergence in predicting at the edge of subsidence basin. In recent years, many scholars have studied a lot of subsidence monitoring methods in the coal mine area using the technical advantages of differential interferometry synthetic aperture radar (D-InSAR). But, serious incoherence of interferometry phase occurs because of the large gradient subsidence of mining area, which leads to the inability to accurately obtain large gradient subsidence of surface. Meanwhile, PIM model is more suitable for static prediction of mining subsidence, and has certain defects in dynamic prediction in the process of mining subsidence. In view of the above shortcomings, the improved PIM (IPIM) prediction model was first introduced through improving the PIM model in the paper, and the IPIM-G dynamic prediction model was constructed based on the PIM model and the Gompertz time function for mine-area mining. Then, a method was used to invert the parameters of the IPIM-G dynamic prediction model using time-series superposition results of surface subsidence monitored by the D-InSAR technology, and then the parameters obtained by inversion were applied to the IPIM-G model for mining subsidence prediction. The model was applied to a coal mine in Huaibei mining area, Anhui Province, its average RMSE is 138 mm and the average RMSE at the edge is 2.8 mm. The accuracy of IPIM-G dynamic prediction model is 88% higher than the monitoring results of the D-InSAR technology in obtaining the gradient subsidence information of the subsidence basin in the mining area. The results show that the model proposed in this paper can provide theoretical support for mining and production planning in the mining area.

Automated summary

Obtaining timely and accurate information of surface subsidence caused by mining is of great significance. The Probability Integral Method (PIM) model is widely used for mining subsidence prediction in China, but it has fast convergence issues at the edge of subsidence basin. The existing models also have difficulties in obtaining accurate information on large gradient subsidence and have defects in dynamic prediction. In this paper, an Improved PIM (IPIM) prediction model and IPIM-G dynamic prediction model were proposed, and parameters were inverted using D-InSAR technology for subsidence monitoring to improve prediction accuracy. The results show that the proposed model can provide theoretical support for mining and production planning.