Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano
Abstract
Volcanic tremor is key to our understanding of active magmatic systems, but due to its complexity, there is still a debate concerning its origins and how it can be used to characterize eruptive dynamics. In this study we leverage machine learning techniques using 6 years of continuous seismic data from the Piton de la Fournaise volcano (La Réunion island) to describe specific patterns of seismic signals recorded during eruptions. These results unveil what we interpret as signals associated with various eruptive dynamics of the volcano, including the effusion of a large volume of lava during the August–October 2015 eruption as well as the closing of the eruptive vent during the September–November 2018 eruption. The machine learning workflow we describe can easily be applied to other active volcanoes, potentially leading to an enhanced understanding of the temporal and spatial evolution of volcanic eruptions.
- Authors:
-
[1];
[2];
[2];
[3];
[4];
[5]
- Space Data Science and Systems GroupLos Alamos National Laboratory Los Alamos NM USA, Geophysics GroupLos Alamos National Laboratory Los Alamos NM USA
- Université de Paris, Institut de physique du globe de Paris, CNRS Paris France, Observatoire volcanologique du Piton de la Fournaise, Institut de physique du globe de Paris La Plaine des Cafres France
- Geophysics GroupLos Alamos National Laboratory Los Alamos NM USA
- Observatoire volcanologique du Piton de la Fournaise, Institut de physique du globe de Paris La Plaine des Cafres France
- ISterreUniversité Grenoble Alpes Gières France
- Publication Date:
- Research Org.:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Chemical Sciences, Geosciences & Biosciences Division; European Research Council (ERC)
- OSTI Identifier:
- 1598689
- Alternate Identifier(s):
- OSTI ID: 1597358; OSTI ID: 1598691
- Report Number(s):
- [LA-UR-19-29716]
[Journal ID: ISSN 0094-8276]
- Grant/Contract Number:
- [20180475DR; 89233218CNA000001; 817803]
- Resource Type:
- Published Article
- Journal Name:
- Geophysical Research Letters
- Additional Journal Information:
- [Journal Name: Geophysical Research Letters Journal Volume: 47 Journal Issue: 3]; Journal ID: ISSN 0094-8276
- Publisher:
- American Geophysical Union
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES
Citation Formats
Ren, C. X., Peltier, A., Ferrazzini, V., Rouet‐Leduc, B., Johnson, P. A., and Brenguier, F. Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano. United States: N. p., 2020.
Web. doi:10.1029/2019GL085523.
Ren, C. X., Peltier, A., Ferrazzini, V., Rouet‐Leduc, B., Johnson, P. A., & Brenguier, F. Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano. United States. doi:10.1029/2019GL085523.
Ren, C. X., Peltier, A., Ferrazzini, V., Rouet‐Leduc, B., Johnson, P. A., and Brenguier, F. Tue .
"Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano". United States. doi:10.1029/2019GL085523.
@article{osti_1598689,
title = {Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano},
author = {Ren, C. X. and Peltier, A. and Ferrazzini, V. and Rouet‐Leduc, B. and Johnson, P. A. and Brenguier, F.},
abstractNote = {Volcanic tremor is key to our understanding of active magmatic systems, but due to its complexity, there is still a debate concerning its origins and how it can be used to characterize eruptive dynamics. In this study we leverage machine learning techniques using 6 years of continuous seismic data from the Piton de la Fournaise volcano (La Réunion island) to describe specific patterns of seismic signals recorded during eruptions. These results unveil what we interpret as signals associated with various eruptive dynamics of the volcano, including the effusion of a large volume of lava during the August–October 2015 eruption as well as the closing of the eruptive vent during the September–November 2018 eruption. The machine learning workflow we describe can easily be applied to other active volcanoes, potentially leading to an enhanced understanding of the temporal and spatial evolution of volcanic eruptions.},
doi = {10.1029/2019GL085523},
journal = {Geophysical Research Letters},
number = [3],
volume = [47],
place = {United States},
year = {2020},
month = {1}
}
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DOI: 10.1029/2019GL085523
DOI: 10.1029/2019GL085523
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