A physics-based generalised failure law for forecasting catastrophic landslides

Forecasting catastrophic slope failures is one of the most challenging tasks in landslide hazard analysis. Reliable landslide forecast is essential for civil authorities to effectively inform the public about potential mountain collapses and their timing, facilitating timely evacuations and the implementation of other safety measures. Over the past decades, great efforts have been devoted to develop and deploy high-precision monitoring technologies to observe unstable slope movements. Various empirical or physical approaches have also been proposed to forecast imminent slope collapses, the predictability of which, however, still remains elusive. One major uncertainty arises from the intermittency of geomaterial rupture behaviour, which is typically characterised by a series of progressively shorter quiescent phases interrupted by sudden accelerations, rather than a smooth continuous progression of deformation and damage. This seemingly erratic pattern complicates landslide prediction. Here, we propose a generalised failure law based on the log-periodic power law singularity model for more reliable time-to-failure forecast of catastrophic landslides. Incorporating a discrete hierarchy of time scales and rooted in the fundamental principles of statistical physics, this novel failure law accurately captures the intermittent rupture dynamics of heterogeneous geomaterials at the site scale. It ensures robustness while maintaining a strong connection to the underlying physical processes. By "locking" into the oscillatory structure of rupture dynamics, this parsimonious model transforms intermittency from traditionally perceived noise into essential information to constrain its prediction. We extensively validate this new failure law on a large dataset of 49 historical landslide events, across a wide range of contexts including rockfalls, rockslides, clayslides, and embankment slopes. The results indicate that our method is general and robust, with significant potential to mitigate landslide hazards and enhance existing early warning systems.