Detecting Subtle Nonlinear Changes in Slow-Moving Landslides with a Hybrid Insar-Cpd Framework:Methodological Assessment and Validation

Abstract

Interferometric Synthetic Aperture Radar (InSAR) technology provides high-resolution time-series observation capabilities for monitoring surface slow-moving landslide deformation. Accurately identifying subtle yet critical change points (CPs)—acceleration, destabilization, or stagnation—remains a key challenge in improving the timeliness and reliability of geological hazard early warning systems. To validate the performance limits of CP detection methods under complex environmental conditions, this study integrates InSAR data with advanced Change Point Detection (CPD) techniques. Three slow-moving landslides (Xiongba, Sela, and Gongba) along the Jinsha River Basin were selected, with their subtle surface deformation histories from 2014 to 2022 reconstructed using SBAS-InSAR analysis. The first InSAR-based landslide critical point validation set was established, comprising 752 independently annotated deformation datasets from four geologists. Bayesian optimization was employed to quantitatively delineate the applicability boundaries of the three algorithms—Breaks For Additive Season and Trend (BFAST), Bayesian Estimator of Abrupt change, Seasonal and Trend change (BEAST) and Parallel Autoencoder (PAE). The results reveal that BEAST achieves superior precision (0.93) in noise-free, seasonally dominated environments, whereas PAE demonstrates robust recall (0.64) under real-world and noisy conditions. BFAST prioritizes sensitivity to trend breaks but exhibits higher false positive rates. By employing a multi-model consensus approach, we precisely identified deformation processes associated with seismic swarms and natural dam-break events, and confirmed that the dam-break-induced displacement threshold closely aligns with previous findings. Notably, this study provides the first evidence that local earthquake swarms (Mw 4.6–5.3) can trigger abrupt landslide displacement. Overall, integrating CPD with InSAR time-series effectively detects subtle nonlinear changes missed by threshold methods. Our hybrid validation (simulated data, expert annotations, geological records) shows method effectiveness depends on geodynamic context, requiring scenario-specific selection. Future integration of CPD with high-precision, real-time InSAR will greatly enhance landslide early warning.

Keywords: InSAR time-series analysis, landslide tipping points, change-point detection, expert-annotated dataset, Jinsha River Basin