Articles | Volume 12, issue 3
https://doi.org/10.5194/esurf-12-641-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-12-641-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 May 2024
Research article |
| 03 May 2024
| 03 May 2024
Machine learning prediction of the mass and the velocity of controlled single-block rockfalls from the seismic waves they generate
Institut Terre et Environnement de Strasbourg/ITES, CNRS & University of Strasbourg, 67084 Strasbourg, France
François Noël
Institut des Sciences de la Terre/ISTE, University of Lausanne, Géopolis, 1015 Lausanne, Switzerland
Geological Survey of Norway, 7491 Trondheim, Norway
David Toe
Université Grenoble Alpes, INRAE, LESSEM, 38000 Grenoble, France
Miloud Talib
Institut Terre et Environnement de Strasbourg/ITES, CNRS & University of Strasbourg, 67084 Strasbourg, France
Mathilde Desrues
Institut Terre et Environnement de Strasbourg/ITES, CNRS & University of Strasbourg, 67084 Strasbourg, France
Emmanuel Wyser
Institut des Sciences de la Terre/ISTE, University of Lausanne, Géopolis, 1015 Lausanne, Switzerland
Ombeline Brenguier
Société Alpine de Géotechnique/SAGE, 38160 Gières, France
Franck Bourrier
Université Grenoble Alpes, INRAE, ETNA, 38000 Grenoble, France
Renaud Toussaint
Institut Terre et Environnement de Strasbourg/ITES, CNRS & University of Strasbourg, 67084 Strasbourg, France
SFF Porelab, The Njord Centre, Department of Physics, University of Oslo, P. O. Box 1048, Blindern, 0316 Oslo, Norway
Jean-Philippe Malet
Institut Terre et Environnement de Strasbourg/ITES, CNRS & University of Strasbourg, 67084 Strasbourg, France
Michel Jaboyedoff
Institut des Sciences de la Terre/ISTE, University of Lausanne, Géopolis, 1015 Lausanne, Switzerland
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Short summary
Natural disasters such as landslides and rockfalls are mostly difficult to study because of the impossibility of making in situ measurements due to their destructive nature and spontaneous occurrence. Seismology is able to record the occurrence of such events from a distance and in real time. In this study, we show that, by using a machine learning approach, the mass and velocity of rockfalls can be estimated from the seismic signal they generate.
Natural disasters such as landslides and rockfalls are mostly difficult to study because of the...
