Settlement Monitoring And Parameter Inversion Along Metro Line Based On Time Series InSAR

Due to the limited monitoring range and time-consuming nature of traditional subway settlement monitoring methods,it is difficult to obtain settlement information along the subway line,which can lead to a failure to timely detect safety hazards.Therefore,it is of great significance to use effective methods to monitor settlement along the subway line.Time-series InSAR technology can partially overcome the shortcomings of traditional settlement monitoring methods,such as a lack of data and monitoring difficulties.In this thesis,time-series InSAR technology is used to monitor settlement and invert related settlement parameters along the Chengdu Metro Lines 3,4,and 7.The specific research work and results are as follows:(1)The PS-InSAR and SBAS-InSAR technologies are used to calculate the surface deformation rate field of Chengdu city.The two experimental results are compared based on the spatial distribution of deformation areas and the linear relationship and Pearson correlation coefficient between them to perform cross-validation.The experimental results show that the spatial distribution of deformation areas of the PS-InSAR and SBASInSAR results are basically consistent,the linear relationship is 0.96,and the Pearson correlation coefficient is 0.98,which confirms the high reliability of the two technologies.(2)Based on the time-series InSAR calculation of surface deformation rate,the deformation rate fields within 500 m range along the Metro Lines 3,4,and 7 were extracted,and their spatiotemporal distribution characteristics of subsidence were analyzed,with the main factors causing surface subsidence being dissected.The results show that the deformation rate of the three metro lines mainly ranges from-2mm/y to4mm/y,and the overall surface deformation is relatively stable.The areas with relatively severe subsidence are distributed near Taipingyuan Station,Hongpailou Station,Zhaojuesi South Road Station to Dongwuyuan Station,Xiongmao Avenue to Junqu General Hospital Station,Caiqiao,Chengdu West Station,Southwest Jiaotong University,University of Electronic Science and Technology,Dongpo Road,Shuangdian Road,Huaishudian,and Shizishan Metro Station.The spatial distribution of subsidence areas is highly consistent with the areas of human construction activity,with low correlation with geological movement,groundwater,and temperature changes.(3)A locally averaged decomposition-based ARIMA prediction model is proposed,and the prediction accuracy of the GM(1,1)prediction model,the Holt-Winters prediction model,and the ARIMA prediction model is compared based on the cumulative settlement data of 36 periods calculated by time-series InSAR.The results show that the ARIMA prediction model has the best effect,and based on this,the ARIMA prediction model is improved by integrating the locally averaged decomposition method.The results show that the prediction accuracy of this model is better than that of the ARIMA prediction model.Based on this model,the cumulative settlement amount of six periods from January to June 2023 in typical settlement areas is predicted.(4)Based on the logistic regression model and the Peck settlement trough model,the settlement time characteristics after the operation of subway lines 3,4,and 7,as well as the settlement trough width and maximum settlement in typical settlement areas,were inverted.The results showed that it takes about 3-5 years for the settlement state of the three lines to reach relative stability after the operation,which conforms to the settlement law of subway lines.The settlement trough widths in the settlement monitoring areas of Caiqiao,Chengdu West Station,Huaishudian,and Shuangdian Road Station were approximately 28.43 m,42.56 m,51.85 m,and 36.18 m,respectively,and the maximum settlement amounts were approximately-27 mm,-21 mm,-21 mm,and-28 mm,respectively.The inversion results are consistent with the results of the three-dimensional surface fitting.