- Journal Article
Rights / licenseCreative Commons Attribution-NonCommercial 4.0 International
Automatic identification of debris flow signals in continuous seismic records remains a challenge. To tackle this problem, we use machine learning, which can be applied to continuous real-time data. We show that a machine learning model based on the random forest algorithm recognizes different stages of debris flow formation and propagation at the Illgraben torrent, Switzerland, with an accuracy exceeding 90 %. In contrast to typical debris flow detection requiring instrumentation installed in the torrent, our approach provides a significant gain in warning times of tens of minutes to hours. For real-time data from 2020, our detector raises alarms for all 13 independently confirmed Illgraben events, giving no false alarms. We suggest that our seismic machine-learning detector is a critical step toward the next generation of debris-flow warning, which increases warning times using simpler instrumentation compared to existing operational systems. © 2020. The Authors. Show more
Journal / seriesGeophysical Research Letters
Pages / Article No.
PublisherAmerican Geophysical Union
157551 - Glacial Hazard Monitoring with Seismology (GlaHMSeis) (SNF)
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