FORECASTING THE TRANSITION OF CREEPING LANDSLIDES TO CATASTROPHIC FAILURE

In California, rising temperatures and a predicted increase in wet years are more likely to lead to slow-moving landslides failing catastrophically. Within the Big Sur area, slow-moving landslides are widespread due to mechanically weak rocks and active tectonic plates, where 46 landslides have been identified. The pre-failure deformation is sometimes apparent in retrospect, but it remains a challenge to predict the sudden transition from gradual deformation to runaway acceleration and catastrophic failure. To investigate the spatiotemporal patterns of slow deformation, we apply methods developed to describe the physics of complex systems.

In our analysis, we transform measurements of the study sites, such as ground surface displacement, soil type, and topographic slope, into a spatially-embedded network in which the nodes are patches of ground and the edges that connect them. We focus primarily on weighting the edges using slope and ground deformation time-series from satellite interferometric synthetic aperture radar (InSAR) data. This spatially-embedded network is represented as a multilayer network where each layer represents a time slice captured from InSAR. We use community detection, which identifies strongly-correlated clusters of nodes, to identify patterns of instability. Network metrics, such as the robustness of communities, show an increasing trend in the weeks leading up to catastrophic failure. These methods are promising as a possible technique for highlighting regions at risk of catastrophic failure.