Time-series InSAR-based Subsidence Monitoring and Dynamic Prediction in Mining Areas
The monitoring and prediction of subsidence in mining areas is of great significance for the prevention of geological disasters and the restoration of ecological environment. The dynamic prediction of surface movement deformation in mining is a key issue in the study of subsidence in mining areas. InSAR subsidence monitoring of large deformation areas can obtain high resolution and high accuracy time-series subsidence monitoring results. In this paper,the time-series InSAR surface deformation technique is combined with the power exponential Knothe model to study the dynamic process of surface subsidence in mining areas. This paper first investigates the SBAS-InSAR surface de-formation monitoring technique,the dynamic subsidence model of the mine area,and the geometric equation between InSAR side-view imaging and the mine surface to determine the calculation method of subsidence displacement values. The dynamic prediction model is established by extracting the time series of surface subsidence basin boundary point deformation and inverting the power exponential Knothe time function. Finally,through the 27-view sentinel image of Changping mine area from 2019-2021,the surface deformation image map is obtained by using data processing to extract the boundary point subsidence time series,invert the Knothe model parameters by using genetic algorithm,and ana-lyse them in comparison with GPS-RTK observation data. Based on the validation results of a selected representative subsidence feature point,the time-series InSAR monitoring values agree well with the GPS-RTK values and have practical application value. It can provide a theoreti-cal basis for the establishment of mining subsidence models.
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