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Persistent scatterers synthetic aperture radar interferometry (PS-InSAR) 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 PS-InSAR 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.

Detecting seasonal and trend components in PS-InSAR displacement time series