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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]

The surface deformation monitoring in coastal areas is affected by the uncertainty of flow control, which leads to the low accuracy of surface deformation monitoring. In order to improve the accuracy of surface deformation monitoring in coastal areas, an automatic monitoring method of surface deforma tion in coastal areas based on time series analysis is proposed. The big data structure model of surface deformation in coastal areas is constructed, and the distributed information reconstruction of surface deformation in coastal areas is carried out by using phase space reconstruction and correlation coefficient mining methods. Big data fusion cluster analysis of surface deformation in coastal areas is carried out combined with time series analysis method. The dynamic distribution characteristics of surface deformation big data in coastal areas are extracted, and the clustering and information fusion results of surface deformation characteristics in coastal areas are obtained. The automatic monitoring of surface deformation dynamics in coastal areas is realized. The simulation results show that the accuracy of automatic monitoring of surface deformation in coastal areas is high, and the average confidence level of monitoring is better than that of traditional methods, which improves the adaptability of surface deformation monitoring in coastal areas. [ABSTRACT FROM AUTHOR]