Articles | Volume 21, issue 1
https://doi.org/10.5194/nhess-21-339-2021
https://doi.org/10.5194/nhess-21-339-2021
Research article
 | 
27 Jan 2021
Research article |  | 27 Jan 2021

Near-real-time automated classification of seismic signals of slope failures with continuous random forests

Michaela Wenner, Clément Hibert, Alec van Herwijnen, Lorenz Meier, and Fabian Walter

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Latest update: 14 Jul 2024
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Short summary
Mass movements constitute a risk to property and human life. In this study we use machine learning to automatically detect and classify slope failure events using ground vibrations. We explore the influence of non-ideal though commonly encountered conditions: poor network coverage, small number of events, and low signal-to-noise ratios. Our approach enables us to detect the occurrence of rare events of high interest in a large data set of more than a million windowed seismic signals.
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