Warning of infrastructure deformation anomaly through probabilistic analysis of InSAR observations
Abstract
Interferometric Synthetic Aperture Radar (InSAR) offers a low-cost, wide-area solution for near continuous infrastructure monitoring. However, translating InSAR observations into automated anomaly warnings remains challenging because displacements are relative, affected by uncertainty, and acquired along line of sight (LOS). We introduced a probabilistic warning framework that accounts for these factors. LOS displacements were modelled through a Gaussian distribution. A limit state function was defined to describe the displacement exceedance, and the time-dependent probability of exceedance was evaluated. Instead of a fixed threshold, warnings were identified through abrupt increases in exceedance probability. The framework was validated using three case studies of quay walls in Amsterdam, providing timely anomaly warnings and issued no warnings for the non-collapse case. Results demonstrate the potential of a probabilistic interpretation of InSAR time series for deformation anomaly warning.
Related
- Probabilistic Forecasting of Electric Vehicle Charging in a Time-varying Hierarchical Infrastructure
- Early warning of critical transitions: Distinguishing supercritical and subcritical bifurcations from noisy observations
- GT-PSSM: Unified Probabilistic Framework for Stochastic Dynamics Modeling and Dependency Learning in Multivariate Time Series Anomaly Detection
- Learning joint probabilistic weather forecasts from station observations alone
- Likelihood estimation of an interpretable early-warning sign of critical transitions
BibTeX
@misc{kuai2026warning,
title = {{Warning of infrastructure deformation anomaly through probabilistic analysis of InSAR observations}},
author = {Hao Kuai and Na Hao and Pantelis Karamitopoulos and Alice Cicirello and Giorgia Giardina},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.13552},
eprint = {2610.13552},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.13552},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Warning of infrastructure deformation anomaly through probabilistic analysis of InSAR observations
AU - Hao Kuai
AU - Na Hao
AU - Pantelis Karamitopoulos
AU - Alice Cicirello
AU - Giorgia Giardina
PY - 2026
DA - 2026/10/09
PB - arXiv
DO - 10.48550/arxiv.2610.13552
UR - https://arxiv.org/abs/2610.13552
AB - Interferometric Synthetic Aperture Radar (InSAR) offers a low-cost, wide-area solution for near continuous infrastructure monitoring. However, translating InSAR observations into automated anomaly warnings remains challenging because displacements are relative, affected by uncertainty, and acquired along line of sight (LOS). We introduced a probabilistic warning framework that accounts for these factors. LOS displacements were modelled through a Gaussian distribution. A limit state function was defined to describe the displacement exceedance, and the time-dependent probability of exceedance was evaluated. Instead of a fixed threshold, warnings were identified through abrupt increases in exceedance probability. The framework was validated using three case studies of quay walls in Amsterdam, providing timely anomaly warnings and issued no warnings for the non-collapse case. Results demonstrate the potential of a probabilistic interpretation of InSAR time series for deformation anomaly warning.
ER -