A robust multi-indicator framework for landslide early warning using complementary statistical physics-based diagnostics

Abstract. Landslide early warning remains challenging because many slopes evolve through intermittent, nonlinear, and non-monotonic deformation before catastrophic failure. Here, we develop an integrated early warning framework that combines three statistical physics-based diagnostics: velocity b-value tracking, dragon-king detection, and log-periodic power law singularity (LPPLS) time-to-failure analysis. The velocity b-value captures long-term changes in the distribution of slope displacement rates, dragon-king detection identifies statistically significant extreme velocity outliers, and LPPLS analysis describes the quasi-deterministic evolution towards a finite-time singularity yielding probabilistic estimates of the failure time and its uncertainty. Applied pseudo-prospectively to the Preonzo, Veslemannen, and Stampa landslides, the framework reveals a coherent sequence of precursory signals: b-value decline generally appears first, dragon-king outliers emerge later as failure becomes imminent, and LPPLS forecasts become increasingly constrained during the final acceleration stage. These complementary indicators are integrated into a traffic-light warning scheme that translates complex rupture dynamics into operationally interpretable warning levels. By combining precursory signals across multiple timescales, the proposed framework establishes a robust and physically grounded foundation for next-generation landslide early warning.