Monitoring Subsidence Deformation of Suzhou Subway Using InSAR Timeseries Analysis
Xiaobo Xu, Dezheng Zhao, Chao Ma and 1 more
IEEE Access · 2021
doi:10.1109/access.2020.3047574 (opens in a new tab)
We email you when a new notice or a question about this paper appears.
Abstract
Subway subsidence and the consequent geological hazards have become one of the major concerns especially in China's large cities. Large spatial-scale and temporally continuous measurements of surface deformation due to subway subsidence is the basis of the intelligent management for urban transit, as well as the guarantee for the early warning of geological disasters. In this paper, we used persistent scatterer interferometric synthetic aperture radar (PS InSAR) measurements to study and characterize the deformation process resulted from subway-induced subsidence in the construction and operation periods of Suzhou subway using 24-scene high-resolution TerraSAR data (from December 2009 to April 2015) and 19-scene low-resolution Sentinel-1A SAR data (from May 2017 to February 2020). Our results indicate spatially different subsidence rates at different segments along the Suzhou subway lines 1, 2, 3, 4, 5 and 6. TerraSAR data indicate the maximum subsidence rate of -13 mm/yr, while Sentinel-1A data demonstrate the maximum subsidence rate of -20 mm/yr. The decadal-scale subway subsidence deformation is generally characterized by its multistage dynamic processes. The significant subway subsidence is mainly distributed around interchange hubs at the intersection area of railway lines. The Peck function model is used to fit the subway subsidence displacement profiles, which reveals the normal distribution characteristics of the subway surface subsidence. Based on our timeseries analysis in different time periods, the logistic cycle function is used to invert the subway subsidence timing, which reveals four first-order stages during subway subsidence: slow acceleration period, fast acceleration period, decay period and steady-state period.
Abstract licence: CC BY-NC-ND
Reads
Read by people on this page
None yet.
Checked or read by AI through naulon
None yet.
A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.
Are you an author?
Is this your paper?
Claim it to add your note and be paid when agents cite it. Free.