Large-Scale Surface Deformation Monitoring Using SBAS-InSAR and Intelligent Prediction in Typical Cities of Yangtze River Delta

Abstract

Large-scale short-term monitoring and prediction of surface deformation are of great significance for the prevention and control of geohazards in rapidly urbanizing developing cities. Most studies focus on individual cities, but it would be more meaningful to address large urban agglomerations and consider the relevance of the regions within them. In addition, the commonly used linear fitting prediction methods cannot accurately capture the dynamic mechanisms of deformation. In this study, we proposed an automatic PS extraction method (named PS-SBAS-InSAR) that improves SBAS-InSAR to extract surface deformation and an Informer-based short-term surface deformation prediction method for case studies in 16 typical cities of the Yangtze River Delta (YRD). The results show that PS-SBAS-InSAR successfully extracted accurate surface deformation sequences of the YRD. During the period from January 2019 to January 2021, the YRD experienced a slight deformation with an average deformation rate within [−4, 4] mm/year. Geographically neighboring cities may have associated deformation distributions and similar deformation trends, as indicated by average deformation rate maps and landscape metrics. Both types of deformation (i.e., subsidence/uplift) tend to occur simultaneously, with specific areas of subsidence/uplift occurring in close proximity to areas of concentrated deformation. The Informer model effectively captured the time-series variation in surface deformation, suggesting a slowdown of deformation over the next two months (February 2021–March 2021). Our work contributes to a better understanding of changes and trends in large-scale surface deformation and provides useful methods for monitoring and predicting surface deformation in coastal areas.