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.