Long-Term InSAR Monitoring and Anomaly Detection of Railway Deformation in Shanghai

Abstract

Land subsidence threatens the operational safety of railways in soft-soil plains. This study investigates the spatiotemporal evolution and mechanisms of subsidence along the Beijing–Shanghai Conventional Railway (BSR) and High-Speed Railway (HSR) in Shanghai using 2015–2025 Sentinel-1 imagery. To explicitly decouple macroscopic environmental background subsidence from localized engineering disturbances, we propose a novel framework integrating SBAS-InSAR monitoring, RF-SHAP multi-source attribution, and LightGBM baseline prediction. Results reveal significant deformation heterogeneity governed by foundation designs: the shallow-subgrade BSR experienced a mean subsidence rate of −2.03 mm/yr (with 4.69% extreme pixels), whereas the deep-anchored HSR remained highly stable at −0.81 mm/yr. Attribution analysis demonstrates that anthropogenic factors primarily drive regional deformation, contributing 70.9% to the variance, with distance to the BSR, groundwater levels, and building density identified as core nonlinear predictors. Furthermore, by analyzing dynamic prediction residuals, the framework accurately traced high-risk structural anomalies, successfully isolating −17.9 mm/yr of acute settlement induced by short-term construction and 100–120 mm of cumulative consolidation triggered by long-term static loads. This approach provides a robust, data-driven diagnostic tool to assist in targeted track-bed maintenance for railway safety management.