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Persistent scatterers synthetic aperture radar interferometry (PSInSAR) technology has been applied in the context of deformation monitoring of transportation infrastructures such as bridges, highways, and railways. However, extracting and interpreting information from long-time series displacement obtained using PS-InSAR is a complex and demanding task. In this study, time series analysis was used to post-process the displacement time series derived from PSInSAR using 100 stripmap mode images collected by TerraSAR-X satellite during 13 April 2010 and 13 December 2019. Seasonal and trend decomposition using loess (STL) and variational modal decomposition (VMD) were combined to estimate the different periodic and trend components of the total displacement time series. Temperature correlation was then generated by associating the different period components with the temperature change. The results show that the deformation of Sihui overpass, Sifang overpass and Shibalidian overpass in Beijing Fourth Ring Road highway is obvious. The trend components obtained by STL decomposition of the selected typical points are primarily nonlinear deformation, and the deformation of Sifang overpass, Shibalidian overpass and Liuxiang overpass slows to varying degrees. After decomposing the displacement time series into several intrinsic mode functions (imf) with finite bandwidth using VMD, the thermal expansion phenomenon is pronounced on the main bridge and approach bridge of some overpasses. Seasonal imf1 is an annual periodic that appears to be highly positively correlated with temperature change. Seasonal imf2 is a period term of greater than one year that confirms that the bridges exhibit other periodic seasonal components in addition to the seasonal deformation of annual period. The thermal expansion characteristics of different periodic seasonal components that are explored in this study can provide valuable information for better understanding the thermal expansion evolution process, and these findings are important scientific significance for bridge structure health monitoring. [ABSTRACT FROM AUTHOR]

In order to accurately separate the elastic and inelastic deformation information caused by aquifer compression in the land subsidence signal, and to invert the hydrogeological parameters of high spatial and temporal resolution to better apply the groundwater-ground subsidence model, a CWT (Continuous Wavelet Transform) separation method for aquifer elastic and inelastic deformation signals based on CWT is adopted, and the deformation signal is extracted by InSAR technology. The large-scale synthetic aperture radar dataset obtained by Envisat satellite from 2007 to 2009 is collected to obtain the surface deformation characteristic of the area by SBAS-InSAR technology, and then the independence provided by the observation well is used. Using the independent water level data provided by the observation wells, combined with the vertical InSAR deformation component and the head data, the CWT method is used to separate the periodic deformation signal components and long-term trends. Finally, the isolated signal component is used to invert the elastic and inelastic storage coefficient based on the ground subsidence model. The settlement signal separation method used in this paper makes up for the shortcomings of the two kinds of information in the previous settlement signal that are difficult to separate, which allows for more accurate inversion of aquifer parameters and helps to understand the aquifer parameters and continuously manage groundwater resources. [ABSTRACT FROM AUTHOR]