Time series monitoring and prediction of coal mining subsidence based on multitemporal InSAR technology and GSM-HW model

Abstract

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, a mining subsidence monitoring and dynamic prediction model combining time series stacking differential interferometric synthetic aperture radar (TSS-DInSAR) technology, small baseline subset InSAR technology (SBAS-InSAR), and golden section method-Holt–Winters (GSM-HW) model was proposed. First, based on the nine scene images of Sentinel-1A satellite in the study area, the time series subsidence of 811 work-face in Guobei coal mine from April 5 to July 10, 2021, was obtained using TSS-DInSAR and SBAS-InSAR monitoring technologies, respectively. Then, the original training samples of the GSM-HW model were generated by combining the cumulative surface subsidence results monitored by TSS-DInSAR and SBAS-InSAR, and the monitoring and prediction of surface subsidence were realized using this model. The experimental results show that the application of TSS-DInSAR and SBAS-InSAR monitoring technology to the impact of surface mining in mining areas has certain reliability, and the GSM-HW prediction model can make up for the deficiency of a single HW model in parameter optimization. The maximum fitting accuracy and prediction accuracy of the model for the 10 monitoring pilot sites are 96.9% and 98.4%, respectively, which can provide a reference for the design of surface monitoring and prediction system in the mining area.