Detecting the Deformation Anomalies Induced by Underground Construction using Multiplatform MT-InSAR: A Case Study in To Kwa Wan Station, Hong Kong

Songbo Wu, Bochen Zhang, Hongyu Liang, Chi sheng Wang, Xiaoli Ding, Lei Zhang
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Underground constructions are implemented in cities to relieve the pressure of urbanization. Ground deformation often occurs with improper construction activities. Such deformation can be measured with techniques such as multitemporal interferometric synthetic aperture radar (MT-InSAR). However, the deformation anomalies such as the building horizontal tilt can cause the MT-InSAR line-of-sight (LOS) deformation to tend to zero, the deformed area is likely to be ignored when the individual SAR
more » ... taset is applied. In this study, we explore deformation induced by the underground construction of To Kwa Wan (TKW) station in Hong Kong with an improved MT-InSAR method. Multiplatform SAR datasets from TerraSAR-X, COSMO-SkyMed, and Sentinel-1A are adopted to retrieve comprehensive deformation of ground buildings for stability investigation. The results are validated through crossvalidation between SAR datasets. Both spatial and temporal variations of ground deformation are revealed. Ground buildings suffered significant deformation in vertical and horizontal directions, the maximum deformation is around 30 mm and 40 mm, respectively. Due to the anomalous deformation, the ground buildings around the station are dumped towards the street center. In view of temporal evolution, the deformation is strongly related to the construction stages. The deformation turned to be stable after the completion of the main framework of the TKW station in December 2016. This study suggests that the deformation obtained from individual SAR datasets may sometimes is not noticeable or reliable in the area with complex underground construction. Such complex phenomena can be revealed by using the multiplatform SAR datasets with MT-InSAR. Manuscript received xxx; revised xxx; accepted xxx. Date of publication xxx; date of current version xxx.
doi:10.1109/jstars.2021.3113672 fatcat:xawkaa5jszbxbipr5kgh4kvj7a