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DEAL Springer Nature
Abstract:
Gravitational mass wasting prediction requires understanding of the factors controlling failure. Prior to slope failure, cracks in the weakened rock are thought to grow and coalesce, eventually forming a continuous failure plane. Here, we apply a hidden Markov machine learning model to seismic data, revealing the temporal evolution of cracks prior to a major rockslide event in the Swiss Alps. After prolonged linear increase of the crack cumulative number, an S-shaped crack rate pattern occurred in the day before the rockslide. A simple mechanistic model can explain this behaviour, showing that total crack boundary length is the key factor controlling failure plane evolution immediately before mass movement. Our findings imply that cracks should be treated as 2-D, rather than 1-D objects, and that slope failure can be driven predominantly by internal rather than external processes. Our model offers a novel, physically based approach for early warning of slope failures.
