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Rock slope failure preparation paced by total crack boundary length

Earth Sciences

Rock slope failure preparation paced by total crack boundary length

S. Lagarde, M. Dietze, et al.

This groundbreaking study conducted by Sophie Lagarde, Michael Dietze, Conny Hammer, Martin Zeckra, Anne Voigtländer, Luc Illien, Anne Schöpa, Jacob Hirschberg, Arnaud Burtin, Niels Hovius, and Jens M. Turowski reveals how monitoring seismic data can provide early warnings for rock slope failures. A hidden Markov machine learning model demonstrates the critical role of crack evolution in slope stability, bringing new insights into the mechanisms of natural disasters.... show more
Abstract
Gravitational mass wasting prediction requires understanding of the factors controlling failure. Prior to slope failure, cracks in weakened rock are thought to grow and coalesce, eventually forming a continuous failure plane. Here, a hidden Markov machine-learning model is applied to seismic data to reveal the temporal evolution of cracks prior to a major rockslide event in the Swiss Alps. After a 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 explains this behaviour, showing that total crack boundary length is the key factor controlling failure plane evolution immediately before mass movement. The 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. The model offers a novel, physically based approach for early warning of slope failures.
Publisher
Communications Earth & Environment
Published On
Jun 05, 2023
Authors
Sophie Lagarde, Michael Dietze, Conny Hammer, Martin Zeckra, Anne Voigtländer, Luc Illien, Anne Schöpa, Jacob Hirschberg, Arnaud Burtin, Niels Hovius, Jens M. Turowski
Tags
rock slope failure
seismic data
hidden Markov model
crack evolution
early warning
temporal evolution
mechanistic model
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