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Temporal And Spatial Characteristics Analysis And Prediction Of Ground Subsidence Along Zhengzhou Metro Based On Time Series InSAR

Posted on:2022-07-29Degree:MasterType:Thesis
Country:ChinaCandidate:Y C YeFull Text:PDF
GTID:2480306605456134Subject:Hydraulic engineering
Abstract/Summary:PDF Full Text Request
The monitoring and analysis of ground subsidence along the metro lines are important work to ensure the safety of metro operation.Time series InSAR technology,represented by permanent scatterers interferometry(PS-InSAR),has the advantages of wide coverage,high monitoring accuracy and low acquisition cost.It overcomes the shortcomings of traditional ground subsidence monitoring methods and provides a powerful tool for ground subsidence monitoring along metro lines.Based on the monitoring results of PS-InSAR,this paper analyzed the temporal and spatial characteristics of ground subsidence along Zhengzhou metro,and predicted and analyzed the ground subsidence of typical metro stations by using grey model and LSTM model.The main research contents and achievements are as follows:(1)Generation and verification of surface deformation data in study area.The surface deformation information of the study area from February 2005 to October 2010 and from July 2015 to May 2019 was obtained based on PS-InSAR technology by using35 Envisat ASAR images and 44 Sentinel-1 images.The deformation data show that the subsidence areas are mainly distributed in the north,northeast and east of Zhengzhou city.The measured leveling data and field survey were used to verify the deformation data.The results show that PS-InSAR technology can meet the accuracy requirements of large-scale urban subsidence monitoring.(2)Temporal and spatial characteristics analysis of ground subsidence along Zhengzhou metro.The spatial characteristics of ground subsidence along the metro lines were studied by extracting PS points in a certain range on both sides of Zhengzhou metro lines.It is found that the subsidence sections are mainly concentrated in the east of Zhengzhou.The maximum subsidence rate is more than 20 mm/ y,and the maximum cumulative subsidence is about 80 mm.Through the analysis of longitudinal and cross sections of typical metro sections,it is found that the uneven deformation along line 1is comparatively prominent.According to the radius of subsidence troughs,it is estimated that its sphere of influence on the ground surface is about 50 m?160 m during the construction of the metro.The temporal characteristics of surface deformation along metro are as follows: The changes of PS points in different regions are quite different in time series.The settlement at the center of the subsidence trough is expanding year by year,and the cumulative subsidence at the center increased from 5mm in 2015 to 61 mm in 2019.Finally,the surface deformation along Zhengzhou metro was analyzed and discussed from three aspects of groundwater,built-up area age and geological structure.(3)Prediction and analysis of ground subsidence near typical station.Aiming at the unequal time interval problem of ground subsidence time series data caused by SAR images discontinuity,an equidistant processing method based on inverse distance weight interpolation was proposed.Then,the ground subsidence near typical station was predicted and analyzed based on grey model and LSTM model.Finally,by comparison and evaluation,LSTM model has higher prediction accuracy.The prediction results of LSTM model show that in the next two years,the north side of the new archives of Henan province will continue to sink at a rate of about 0.5 mm / month?This paper analyzed the spatial and temporal characteristics of ground subsidence along Zhengzhou metro by using PS-InSAR method,and predicted and analyzed the ground subsidence of typical metro station.The research results can provide scientific basis for continuous dynamic monitoring of ground subsidence along Zhengzhou metro and the operation and maintenance of metro.
Keywords/Search Tags:Zhengzhou Metro, PS-InSAR, Ground Subsidence, Grey Model, Long Short Term Memory Network
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