Mapping Urban Excavation Induced Deformation in 3D via Multiplatform InSAR Time-Series

Abstract

Excavation of a subway station and rail crossover cavern in downtown Los Angeles, California, USA, induced over 1.8 cm of surface settlement between June 2018 and February 2019 as measured by a ground-based monitoring system. Point measurements of surface deformation above the excavation were extracted by applying Interferometric Synthetic Aperture Radar (InSAR) time-series analyses to data from multiple sensors with different wavelengths. These sensors include C-band Sentinel-1, X-band COSMO-SkyMed, and L-band Uninhabited Aerial Vehicle SAR (UAVSAR). The InSAR time-series point measurements were interpolated to continuous distribution surfaces, weighted by distance, and entered into the Minimum-Acceleration (MinA) algorithm to calculate 3D displacement values. This dataset, composed of satellite and airborne SAR data from X, C, and L band sensors, revealed previously unidentified deformation surrounding the 2nd Street and Broadway Subway Station and the adjacent rail crossover cavern, with maximum vertical and horizontal deformations reaching 2.5 cm and 1.7 cm, respectively. In addition, the analysis shows that airborne SAR data with alternative viewing geometries to traditional polar-orbiting SAR satellites can be used to constrain horizontal displacements in the North-South direction while maintaining agreement with ground-based data.

Tunneling- and dewatering-induced rapid differential ground rebound and delayed subsidence measured by InSAR in an urban environment

“…37. (a) P1, (b) P2, (c) P3, (d) P4, (e) P5, (f) P6, (g) P7, (h) P8, (i) P9, (j) P10, (k) P11, and (l) P12. Sentinel-1 data from European Space Agency.…”

“…Tunneling-induced ground subsidence has been studied by researchers using various methods, such as in situ monitoring, analytical or numerical modeling, interferometric synthetic radar (InSAR) monitoring, and subsidence prediction by machine learning, e.g., Refs. 3–8. Potential hazards induced by post-dewatering rebound are less often considered and rarely have they been studied with detailed spatial geodetic data in an urban setting.…”

“…The effects of groundwater changes concerning surface deformation have been well documented using InSAR at both regional scales 9 – 17 and for individual construction projects 18 – 20 InSAR techniques have successfully been applied to measure deformation induced by tunneling activities in both hard rock 21 – 23 and soft rock or sediment rich environments 6 , 24 , 25 . Studies have shown that groundwater recharge can cause surface uplift, the duration of which depends on factors such as geology, degree of aquifer depletion, and groundwater pumping rates, 9 , 15 , 26 , 27 though these studies often rely on measurements from individual interferograms and have not investigated tunneling related rebound in urban settings.…”

Physics-Informed Ensemble Machine Learning Framework for Improved Prediction of Tunneling-Induced Short- and Long-Term Ground Settlement

“…A great deal of research effort has been devoted to the study of settlements caused by tunneling through soft ground. Machine learning (ML) techniques are becoming a promising alternative to predict tunneling-induced ground deformation based on ground observations and measurements besides remote sensing monitoring [2][3][4][5][6]. However, ML applications in predicting tunneling-induced ground behavior are less common due to the limitation of available data size, ML interpretability, and physics understanding of the mechanisms of ground deformation.…”