Benchmarking Seismic-Based Feature Groups to Classify the Cotopaxi Volcanic Activity

Pérez, Noel; Venegas, Pablo; Benítez, Diego S.; Grijalva, Felipe; Lara-Cueva, Román; Ruiz, Mario

doi:10.1109/lgrs.2020.3028193

Cited by 9 publications

(2 citation statements)

References 19 publications

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“…Each decision tree in the "forest" is therefore different, and the model combines hundreds (if not thousands) of decision trees. Random forest is now successfully used in seismology for automated source classification (Provost et al, 2017;Hibert et al, 2017c;Maggi et al, 2017;Malfante et al, 2018;Hibert et al, 2019;Ao et al, 2019;Pérez et al, 2020;Wenner et al, 2021;Chmiel et al, 2021). However, the random forest algorithm can also be used to estimate continuous values and thus perform regression analyses.…”

Section: Machine Learning: Using Random Forests As a Regression Toolmentioning

confidence: 99%

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

Hibert,

Noël,

Toe

et al. 2024

Earth Surf. Dynam.

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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 us 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 is still poorly understood. We conducted a controlled rockfall experiment in the Riou Bourdoux torrent (southern 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 stereophotogrammetry. 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 in that it does not require the source to be localised, nor does it require a high-resolution velocity model or 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 it might eventually lead to better constraints on the physical models in the future.

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Section: Machine Learning: Using Random Forests As a Regression Toolmentioning

confidence: 99%

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

Hibert,

Noël,

Toe

et al. 2024

Earth Surf. Dynam.

View full text Add to dashboard Cite

show abstract

“…Random Forests is now successfully used in seismology for automated source classification (Provost et al, 2017;Hibert et al, 2017c;Maggi et al, 2017;Malfante et al, 2018;Hibert et al, 2019;Ao et al, 2019;Pérez et al, 2020;Wenner et al, 2021;Chmiel et al, 2021). However the Random Forests algorithm can also be used to estimate continuous values and thus perform regression analyses.…”

Section: Machine Learning: Using Random Forests As a Regression Toolmentioning

confidence: 99%

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

Noël¹,

Toe

Talib

et al. 2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

Deep learning and multi-station classification of volcano-seismic events of the Nevados del Chillán volcanic complex (Chile)

Ferreira,

Curilem,

Gomez

et al. 2023

Neural Comput & Applic

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Benchmarking Seismic-Based Feature Groups to Classify the Cotopaxi Volcanic Activity

Cited by 9 publications

References 19 publications

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

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

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

Deep learning and multi-station classification of volcano-seismic events of the Nevados del Chillán volcanic complex (Chile)

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