Application of Gray-Markov Model to Land Subsidence Monitoring of a Mining Area

Abstract

Land subsidence monitoring in mining areas is one of the main applications of surface deformation monitoring, which is of great significance for safety production. Using the IPTA (Interferometric Point Target Analysis) time-series InSAR (Interferometry Synthetic Aperture Radar) method, land subsidence data of the new exploration area in the Weizhou mining area were analyzed and compared with static GPS (Global Positioning System) monitoring data from 2017 to 2020. Gray-Markov model was established by combining the gray prediction model with the Markov model to predict the surface subsidence of the mining area. The results show that (1) InSAR data has high accuracy and application potential in prediction of long-term surface deformation in mining areas; (2) The Gray-Markov model can better reflect the volatility and practicality of subsidence data in mining areas; (3) The prediction results have high accuracy, and the Gray-Markov model can serve as an effective guide for long-term surface deformation monitoring and safety management.