Physics-based uncertainty modeling of deep-seated landslides using InSAR: A case of El Forn (Andorra)

Abstract

Deep-seated landslide monitoring can require extensive insitu monitoring tools, typically involving equipping boreholes with extensometers, thermometers, and piezometers – proving to be an expensive and labor-intensive task. This work focuses on assessing deep-seated landslide stability by using the physics-based modeling, in partnership with Interferometric Synthetic Aperture Radar (InSAR), as a diagnostic tool for assessing stability in remote regions. We use the case of the insitu monitored El Forn landslide in Canillo, Andorra. We used available Sentinel-1 data to create a velocity map from deformation time series in 2019 and inputted it into a calibrated physics-based predictive model. Using the correlation between the model’s velocity, the insitu observed velocity and the velocity derived from InSAR, we create a normalized real-time risk map of the landslide.