Abstract
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. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000472319Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
Geophysical Research LettersBand
Seiten / Artikelnummer
Verlag
American Geophysical UnionOrganisationseinheit
09558 - Walter, Fabian (ehemalig) / Walter, F. ((former))
Förderung
157551 - Glacial Hazard Monitoring with Seismology (GlaHMSeis) (SNF)