Real-Time Forecast of Catastrophic Landslides via Dragon-King Detection

Catastrophic landslides characterized by runaway slope failures remain difficult to predict. Here, we develop a physics-based framework to prospectively assess slope failure potential. Our method builds upon the physics of extreme events in natural systems: the extremes so-called dragon-kings (e.g., slope tertiary creeps prior to failure) exhibit statistically different properties than other smaller-sized events (e.g., slope secondary creeps). We develop statistical tools to detect the emergence of dragon-kings during landslide evolution, with the secondary-to-tertiary creep transition quantitatively captured. We construct a phase diagram characterizing the detectability of dragon-kings against black-swans and informing on whether the slope evolves toward a catastrophic or slow landslide. We test our method on synthetic and real data sets, demonstrating how it might have been used to forecast three representative historical landslides. Our method can in principle considerably reduce the number of false alarms and identify with high confidence the presence of true hazards of catastrophic landslides.

Automated summary

Researchers have developed a physics-based framework to assess the potential risk of landslide slope failures. By studying extreme events in natural systems, they have developed statistical tools to detect the emergence of "dragon-kings" events during landslide evolution. They have also constructed a phase diagram to determine whether the slope is evolving towards a catastrophic or slow landslide.