Inversion of Subsidence Parameters and Prediction of Surface Dynamics under Insufficient Mining

Abstract

By combining the advantages of InSAR, Probabilistic Integral Method and Genetic Algorithm, an improved method for dynamic prediction of probability integral parameters is proposed to realize subsidence inversion and prediction in insufficient mining. Firstly, InSAR is used to obtain the time series information of surface deformation in goaf. Then, a genetic algorithm-based parameter inversion model is constructed to invert the subsidence parameters such as subsidence coefficient and influence radius. After that, a dynamic prediction function is established to obtain the complete surface subsidence pattern and dynamic change trend of the mining area. Taking a goaf in Shanxi Province as the experimental object, Sentinel-1A(S-1A) image as the data source, combined with PIM and InSAR data, the parameter inversion model is used to successfully obtain the dynamic change process of mining subsidence parameters. The results show that the dynamic prediction function can achieve a certain effect on surface prediction in insufficient mining, and the parameter inversion model based on genetic algorithm has a high inversion accuracy, which provides a basis for surface prediction in insufficient mining.