Preprints
https://doi.org/10.5194/egusphere-2022-522
https://doi.org/10.5194/egusphere-2022-522
12 Jul 2022
 | 12 Jul 2022

Machine learning prediction of the mass and the velocity of controlled single-block rockfalls from the seismic waves they generate

Clément Hibert, François Noël, David Toe, Miloud Talib, Mathilde Desrues, Emmanuel Wyser, Ombeline Brenguier, Franck Bourrier, Renaud Toussaint, Jean-Philippe Malet, and Michel Jaboyedoff

Abstract. Understanding the dynamics of slope instabilities is critical to mitigate the associated hazards but their direct observation is often difficult due to their remote locations and their spontaneous nature. Seismology allows to get unique information on these events, including on their dynamics. However, the link between the properties of these events (mass and kinematics) and the seismic signals generated are still poorly understood. we conducted a controlled rockfall experiment in the Riou-Bourdoux torrent (South French Alps) to try to better decipher those links. We deployed a dense seismic network and inferred the dynamics of the block from the reconstruction of the 3D trajectory from terrestrial and airborne high-resolution stereo-photogrammetry. We propose a new approach based on machine learning to predict the mass and the velocity of each block. Our results show that we can predict those quantities with average errors of approximately 10 % for the velocity and 25 % for the mass. These accuracies are as good as or better than those obtained by other approaches, but our approach has the advantage of not requiring to localize the source and an a priori knowledge of the environment, nor of making a strong assumption on the seismic wave attenuation model. Finally, the machine learning approach allows us to explore more widely the correlations between the features of the seismic signal generated by the rockfalls and their physical properties, and might eventually lead to better constrain the physical models in the future.

Clément Hibert, François Noël, David Toe, Miloud Talib, Mathilde Desrues, Emmanuel Wyser, Ombeline Brenguier, Franck Bourrier, Renaud Toussaint, Jean-Philippe Malet, and Michel Jaboyedoff

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-522', Anonymous Referee #1, 31 Jan 2023
  • RC2: 'Comment on egusphere-2022-522', Anonymous Referee #2, 18 Feb 2023
  • EC1: 'Comment on egusphere-2022-522', Tom Coulthard, 28 Feb 2023
  • AC1: 'Comment on egusphere-2022-522', Clement Hibert, 16 Mar 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-522', Anonymous Referee #1, 31 Jan 2023
  • RC2: 'Comment on egusphere-2022-522', Anonymous Referee #2, 18 Feb 2023
  • EC1: 'Comment on egusphere-2022-522', Tom Coulthard, 28 Feb 2023
  • AC1: 'Comment on egusphere-2022-522', Clement Hibert, 16 Mar 2023
Clément Hibert, François Noël, David Toe, Miloud Talib, Mathilde Desrues, Emmanuel Wyser, Ombeline Brenguier, Franck Bourrier, Renaud Toussaint, Jean-Philippe Malet, and Michel Jaboyedoff
Clément Hibert, François Noël, David Toe, Miloud Talib, Mathilde Desrues, Emmanuel Wyser, Ombeline Brenguier, Franck Bourrier, Renaud Toussaint, Jean-Philippe Malet, and Michel Jaboyedoff

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Latest update: 12 Apr 2024
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Short summary
Natural disasters such as landslides and rock falls 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 using a machine learning approach, the mass and velocity of rockfalls can be estimated from the seismic signal they generate.