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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.

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Playback language: English
Abstract
This study investigates the factors controlling rock slope failure using seismic data from the Illgraben catchment in Switzerland. A hidden Markov machine learning model was applied to seismic data to reveal the temporal evolution of cracks before a major rockslide. The results show an S-shaped crack rate pattern in the day before the rockslide, explained by a mechanistic model indicating that total crack boundary length is the key factor controlling failure plane evolution. This suggests that cracks should be treated as 2-D objects, and that slope failure can be internally driven. The model offers a novel 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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