Monitoring and analysis of ground subsidence in Shanghai based on PS-InSAR and SBAS-InSAR technologies

• Published in: Scientific reports Vol. 13; no. 1; pp. 8031 - 17
• Main Authors: Zhang, Zhihua, Hu, Changtao, Wu, Zhihui, Zhang, Zhen, Yang, Shuwen, Yang, Wang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.05.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 704/172; 704/2151/213; Decision making; Emergency preparedness; Geology; Humanities and Social Sciences; Interferometry; Land subsidence; Monitoring methods; multidisciplinary; Radar; Science; Science (multidisciplinary); Urban areas
• DOI: 10.1038/s41598-023-35152-1
• ISSN: 2045-2322

Shanghai is susceptible to land subsidence due to its unique geological environment and frequent human activities. Traditional leveling techniques are not sufficient for monitoring large areas of land subsidence due to the time-consuming, labor-intensive, and expensive nature of the process. Furthermore, the results of conventional methods may not be timely, rendering them ineffective for monitoring purposes. Interferometric Synthetic Aperture Radar (InSAR) technology is a widely used method for monitoring ground subsidence due to its low cost, high efficiency, and ability to cover large areas. To monitor the surface sink condition of Shanghai over the past 2 years, monitoring data were obtained through the technical processing of 24 images from Sentinel-1A data covering Shanghai from 2019 to 2020 using the Persistent Scatterer (PS-InSAR) and Small Baseline Subset (SBAS-InSAR) technique. The ground subsidence (GS) results were extracted via PS and SBAS interferometry processing, while Shuttle Radar Topography Mission data were used to correct the residual phase. According to PS and SBAS methods, the maximum ground subsidence in the study area reached 99.8 mm and 47.2 mm, respectively. The subsidence rate and the accumulated amount of subsidence derived from the monitoring results revealed the urban area in Shanghai to be principally characterized by uneven GS, with multiple settlement funnels being found to be distributed across the main urban area. Moreover, when compared with the historical subsidence data, geological data, and urban construction distribution data, the individual settlement funnels were observed to correspond to those data concerning the historical surface settlement funnel in Shanghai. By randomly selecting GS time-series data regarding three feature points, it was determined that the morphological variables of the GS remained largely consistent at all time points and that their change trends exhibited a high degree of consistency, which verified the reliability of the PS-InSAR and SBAS-InSAR monitoring method. The results can provide data support for decision making in terms of geological disaster prevention and control in Shanghai.