GMM-EM Based Time-Series InSAR Landslide Hazard Identification And Deformation Trend Prediction

As a kind of geological disaster,landslide has great destructive effect on human production and life.Long-term and effective monitoring of landslide prone areas and accurate identification of potential hazard areas are important prerequisites for early warning and disaster assessment of landslide prone areas and effective countermeasures.However,the complexity of landslide areas,the suddenness of landslide hazards,and the uncertainty of landslide triggering factors make the early identification and disaster assessment of landslide hazards constrained to a certain extent,and InSAR technology has shown great potential in the application of landslide hazard identification and disaster assessment due to its all-day,all-weather earth observation capability and the ability to detect small surface deformation.However,the InSAR technique suffers from the problem of insufficient number and reliability of coherent point selection to recover accurate surface deformation information in the face of complex terrain and atmospheric delay.In this paper,based on the advantages and limitations of time-series InSAR for surface deformation detection,we carry out a research to improve the accuracy of time-series InSAR inversion of surface deformation information by increasing the number and quality of coherent points selection.Based on the verification of the accuracy and reliability of coherent point selection and deformation inversion based on GMM-EM algorithm,we have completed the identification and deformation trend analysis of landslide potential areas in Jinsha River Basin,Tibet.The specific work and results are as follows:(1)To address the limitations of the accuracy and number of high coherence points selection in the time-series InSAR technique,this paper proposes a GMM-EM algorithm combined with PS points to realize the SBAS-InSAR method of selecting coherence points.Firstly,the GMM-EM algorithm statistically classifies the coherence coefficient image element points in the study area,and then the coherence coefficient threshold is set on the basis of retaining homogeneous high coherence points combined with PS points.(2)The coherence points selected by this method are validated with the actual distribution of surface features,and the results show that the number and reliability of coherence points can be improved based on this method,and the interpolation operation of the retained coherence points can effectively reduce the problem of misselection or omission of high coherence points in the high and low coherence intersection area.(3)The deformation monitoring results obtained by this paper are validated by combining the GPS monitoring station data,and the final deformation monitoring results are more consistent with the monitoring station observation data than the traditional SBAS-InSAR technique,and the error reaches mm level.(4)Based on the verification of the reliability of the accuracy of the GMM-EM algorithmbased time-series InSAR inversion of deformation features,the proposed method is further combined with the proposed method to perform the deformation inversion of the Jinsha River basin in Tibet and to detect the hidden dangers existing in the region according to the deformation distribution.The results show that the resurrection location of the Wodagu landslide in the Jinsha River basin and the existing hidden danger areas in the Bago landslide area are shown,and the correctness of the hidden danger locations is verified by combining with the field survey data.