Urban Rail Settlement Monitoring Method Based on Variational Bayesian Learning Time Series InSAR Data
In order to monitor urban rail settlement reasonably and ensure the safety of urban rail transit,a method for monitoring urban rail settlement based on variational Bayesian learning time-series InSAR data is proposed.Taking railway tracks in urban environments as an example,a series of X-segment 79 shorter wavelength SAR images were obtained using permanent scatterer InSAR technology,spanning from January 2016 to September 2023.Through variational Bayesian learning,we filtered SAR images,selected primary and secondary images,and processed them with interferometry,obtaining interferometric phase maps.Then,deformation data of urban rail target points were obtained by selecting permanent scatterers.After geocoding the obtained deformation information,it is placed on an appropriate geospatial data analysis platform for visualization and analysis processing.By converting to vertical deformation data,it can reflect the settlement status of urban tracks in the study area.The test results indicate that approximately 45280 target points can be obtained within the study area,including data within a range of 35 meters on each side of the track.Based on these data,we can conduct urban rail settlement observation work,obtain the distribu-tion of urban rail settlement rate in the study area,and display the trend of urban rail settlement during the observation period.
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维普
