Application of Optimized Grey-Markov Model to Land Subsidence Monitoring With InSAR

Abstract

Land subsidence prediction in mining areas is one of the most important applications of land deformation monitoring, which is significance for safe production. We used interferometric point target analysis (IPTA) timing series interferometry synthetic aperture radar (InSAR) processing technology to analyze the land subsidence results for the Xinfa mining area from 2017 to 2020; and compared them to global positioning system (GPS) static monitoring data. We proposed a residual correction theory based on deviation coefficient using the grey prediction and Markov models, and an optimized Grey-Markov model (RGM-M model) was established to predict the land subsidence of the mining area. Our results show that: (1) The maximum difference between InSAR timing processing results and GPS monitoring data in the same period is 10.91mm; they have roughly the same subsidence trend, indicating that IPTA timing series InSAR technology are strongly reliable in mining deformation. (2) Compared to the traditional Grey-Markov model, the improved residual correction and dynamic assignment of the Grey-Markov model improves the prediction accuracy. The optimized residual correction and dynamic empowerment of the Grey-Markov model prediction results are more suitable for the actual fluctuation of land subsidence value in the mining area. The maximum root mean square error of the prediction results is 0.751mm, and the maximum average absolute percentage error is 7.46%, which has a certain guiding significance for the work of monitoring, prediction and safety management of land deformation in the mining area.

Keywords

• Dynamic empowerment
• Grey-Markov model
• mining subsidence
• prediction
• residual correction
• timing series InSAR