Remote landslide risk assessment fusing data-driven and physics-based approaches

Abstract

Deep-seated landslides involve the movement of large, slow-moving, masses of soil creeping at often imperceivably slow speeds before catastrophically collapsing. Driven by a combination of natural and anthropogenic factors, these extreme slope failures events pose significant societal risks, manifesting in both economic losses and human casualties. These mass movements slip along a heavily-deformed shearing surface, known as the shear band. Because these sliding surfaces are typically made up of clay, they are especially sensitive to pressure and temperature changes. For several decades, these landslides were monitored via borehole instrumentation, which measured parameters such as pore pressure, displacement, and temperature. However, drilling and instrumenting these boreholes are invasive, labor-intensive, and expensive -- assuming that it is logistically feasible to access these sites, which are often remote and difficult to reach. This thesis offers a detailed exploration and deployment of a novel application of Interferometric Synthetic Aperture Radar (InSAR) data for monitoring and modeling the El Forn landslide in Andorra by merging a physics-based model for deep-seated landslide evolution, borehole data, and InSAR data, utilizing the Alaska Satellite Facility (ASF) Vertex platform and the European Ground Motion Service (EGMS) platform. This work begins by focusing on the performance of InSAR data from the EGMS platform with ASF's On Demand InSAR processing tools, highlighting the trade-off between precision and accuracy in the consideration of different resolution InSAR data. InSAR data is then intertwined with data-driven methodologies for a nuanced modeling of the El Forn landslide in Andorra through the calibration of a temperature-driven physics-based model using in situ data, optimized parameters were obtained to inform the creation of a risk map built through ordinary kriging and InSAR data. In order to create a more holistic metric for landslide stability, this research introduces a population risk mapping tool and consideration of a multi-objective optimization framework. This framework aims to guide communities and policymakers in understanding the risks associated with deep-seated landslides, facilitating informed decisions regarding disaster preparedness.