Monitoring Of Land Subsidence In Ming Areas Based On Series InSAR

With the rapid development of my country’s economy,people’s demand for energy continues to expand.Mining is the main way to obtain energy.Mineral mining is an underground operation,and the coal buried deep in the ground is transported to the ground through underground tunnels.Therefore,the safety of underground coal mines should continue to be studied in depth.As the excavation depth increases,the overlying strata of the coal seam collapse.Therefore,it is necessary to conduct deeper research on the underground operation of mining,and take effective measures to prevent the occurrence of subsidence.China’s economy is developing rapidly,and the country’s demand for energy is also rapidly increasing.Mining is the most important way to obtain energy.Most of the mining work is carried out underground,and the mineral resources buried deep in the ground are transported to the land through underground tunnels,which requires a sufficient understanding of the displacement and deformation of the underground rock mass mining process.Therefore,it is urgent to provide effective and safe measures for underground mining technology.InSAR technology has all-day,all-weather observation and can achieve high-precision monitoring of large-scale surface deformation.Compared with traditional observation technology,InSAR technology has high spatial resolution and high monitoring accuracy,and InSAR technology has large-scale monitoring capabilities that traditional monitoring technology does not have.SAR images can be repeatedly observed according to the revisit cycle of the satellite,so as to achieve high-efficiency real-time dynamic monitoring.InSAR technology can make up for the safety of manual field measurement,while saving manpower,material and financial resources.This paper focuses on the SBAS-In SAR technology to monitor the method of mining subsidence.Aiming at the problem that the selection of ground control points(GCP)in SBAS-In SAR technology is affected by the large image of human subjective factors,the PS points obtained in PS-InSAR technology are screened as the ground control points(GCP)of SBAS-InSAR to improve SBAS-InSAR technology,and cross-compared the monitoring results of the three-InSAR technologies,and finally established the SSA-BP algorithm to establish the mining subsidence prediction model in the mining area.The main research work includes:(1)The geometric principle of In SAR technology is discussed,the basic principle of SBAS-In SAR technology and the basic principle of PS-InSAR technology are introduced,and the two technologies are compared and analyzed.(2)Taking Zhangjiamao mining area in Yulin City,Shaanxi Province as the research area,using 21 scene C-band sentinel-1A data,using traditional SB AS-InSAR technology,traditional PS-In SAR technology,and improved SBAS-In SAR technology to process the data In order to achieve cross-validation,the reliability of the results is verified through the comparison of results and field conditions.(3)The results of traditional SBAS-InSAR technology,traditional PS-InSAR technology,and improved SBAS-In SAR technology are used to verify the accuracy of ground level observation points,and the results show high correlation.The accumulated deformation variables of the monitoring points obtained are modeled to realize the three-dimensional reconstruction of the goaf subsidence area.The processing results show that there are many areas in the study area where massive subsidence occurs,among which the northern area is the most serious subsidence,and the maximum subsidence rate can reach-146.76mm/a.(4)It is proposed to use the time series InSAR technology and the SSA-BP algorithm to establish a mining subsidence prediction model in the mining area.Combined with the time series subsidence monitored by the improved SBAS-In SAR technology,the points are uniformly selected in the direction and inclination of the working face,and the mining subsidence dynamic of the mining area is established.Predict the model and evaluate the accuracy of the prediction results.