75 / 2023-03-31 23:35:40

Research on Mining Area Subsidence Monitoring and Dynamic Prediction Based on DS-InSAR

DS-InSAR；Exponent Knothe Model；Genetic Algorithm

The surface subsidence caused by coal mining will cause serious environmental problems, and an effective monitoring and prediction system is indispensable. Aiming at this phenomenon, Synthetic Aperture Radar interferometry (InSAR) technique for distributed scatterers (DS)was applied in coal deformation mining. By identifying DS to increase the density of observation  points,  and permanent  type  scatterers  (PS),  the combination  of  unified  calculating  framework was established to improve  the  deformation  variables calculating precision. The exponent Knothe model is widely used to analyze the surface subsidence process at a single point,in which is consistent with the actual surface subsidence process.The method combined InSAR technology with exponent Knothe model was proposed to extract the surface deformation information caused by mining activities. Firstly the functional relationship is established between the LOS deformation and the parameters of exponent Knothe model. Then the least square method is adopted to estimatethe parameters. According to the surface deformation value of the mining area, the Genetic Algorithm(GA) was used to retrieve the parameters of the exponential knoth time function model, and the subsidence curve model of the surface monitoring points is established. Finally,the final deformation value of the mining area surface points was  predicted according to the established subsidence model. Sentinel-1a radar data of 28 scenes in Yaan mining area were selected for experimental analysis. The results showed that DS-InSAR technology could well monitor surface subsidence of mining area with a maximum subsidence rate of 75 mm/y. The results show that the Knothe time model can more truly reflect the dynamic  process of the surface with the mining time. The average relative standard deviation between the predicted value and the measured value is only 3.22%, which verifies the accuracy and reliability of the improved time model.