Research On Deformation Disaster Monitoring And Prediction Method Based On InSAR And Its Application

Mining subsidence and urban surface subsidence are two typical surface deformation disasters.They will cause environmental pollution,harm and huge economic losses to industrial and agricultural production,transportation,urban infrastructure construction and people's life.It is important to carry out monitoring and prediction of surface deformation hazards to protect the safe production and life of people in the subsidence area.In view of the fact that the traditional geodetic methods can not observe in a large range,long time series and high density.Interferometric Synthetic Aperture Radar(InSAR)makes up for the shortcomings of the traditional methods.With its advantages of high precision,large range and all-weather,InSAR has been widely used in surface deformation monitoring.However,there are still some limitations in monitoring surface deformation by InSAR technology:On the one hand,InSAR technology can only obtain LOS deformation,and its monitoring results can not truly reflect the three-dimensional surface deformation,which restricts the application of InSAR Technology in mining area monitoring;On the other hand,although InSAR technology can extract the temporal deformation information of urban surface,it can not effectively predict the future temporal deformation data.In view of the above problems,this paper takes the two deformation disasters of mining subsidence and urban surface subsidence as the research object,and carries out the research and exploration of deformation disaster monitoring and prediction method based on InSAR technology.The main research contents are as follows:(1)An integrated method of surface deformation monitoring and prediction in D-InSAR mining area based on Boltzmann function is proposed.In this paper,the Boltzmann function prediction model with better fitting ability to the edge of the subsidence basin is selected,combined with the geometric projection relationship between LOS deformation and three-dimensional deformation,the D-InSAR monitoring deformation equation assisted by Boltzmann function is established,and the Shuffled Frog Leaping Algorithm(SFLA)is introduced to solve the prediction parameters in the process,so as to construct the integrated method of mining subsidence D-InSAR monitoring and prediction.The feasibility and correctness of the method are verified by simulation experiments,and the anti error ability of the model is discussed.The experiments show that the method has a certain anti error ability.Using this method,the predicted parameters of 1312(1)working face under insufficient mining in Huainan mining area are obtained.The results show that the mean square error of predicted subsidence is 97.1mm,about 3.09%of the maximum subsidence value;The estimated mean square error of horizontal movement is 46.1mm,which is about 4.1%of the maximum horizontal movement value.Simulation experiments and real examples show that the method constructed in this paper is more robust and can improve the accuracy of 3D deformation prediction.(2)An urban deformation monitoring and prediction model incorporating differential interferometric short baseline set time series analysis technique(SBAS-InSAR)and recurrent neural network(Elman)model is developed.Taking Hefei city as the research area,this paper selects 34 Sentinel-1A data,uses SBAS-InSAR technology to obtain the time series deformation information of Hefei surface from 2018 to 2021,cross verifies it with permanent scatterer synthetic aperture radar interferometry(PS-InSAR)technology.A total of 440 characteristic points with the same name are selected in the experiment.Taking the annual average deformation rate of PS point as the horizontal axis and the annual average deformation rate of sdfp point as the vertical axis,the correlation coefficient between them is 0.9160,showing a high correlation.Then,the key settlement areas of Hefei are analyzed.In order to predict the subsequent surface deformation,the Elman neural network model is selected,and the Shuffled Frog Leaping Algorithm(SFLA)is used to optimize the neural network weight and threshold.The SFLA-Elman network prediction model is established to predict the typical settlement area of Hefei.The experimental research shows that the prediction accuracy is ideal,which can be used for urban spatial planning It provides a certain theoretical reference for disaster prevention and reduction.(3)A surface deformation disaster monitoring and prediction system based on InSAR technology is developed.Taking two typical geological disasters of mining subsidence and urban surface deformation as the research object,the system is mainly composed of the following four modules:?Mining subsidence monitoring system;?Mining subsidence prediction system;?Urban surface deformation monitoring system;?Urban surface deformation prediction system.Based on InSAR technology and mining subsidence prediction theory,the function of the system covers monitoring and prediction of mining surface subsidence,extraction of moving deformation information and prediction of large-scale urban surface deformation.The example shows that the system can run smoothly and output the results visually,which meets the expected requirements and has certain practicability.Figures[56]Tables[9]References[126]...