Near-real-time multiparametric seismic and visual monitoring of explosive activity at Sabancaya volcano, Peru

This study presents the development of a multiparametric system that utilizes artificial intelligence techniques to identify and analyze volcanic explosions in near real-time. The study analyzed 1343 explosions recorded between 2019 and 2021, along with seismic, meteorological, and visible image dat...

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Autores principales: Centeno, Riky, Gómez-Salcedo, Valeria, Lazarte, Ivonne, Vilca-Nina, Javier, Osores, María Soledad, Mayhua-Lopez, Efraín
Formato: Artículo
Lenguaje:Inglés
Publicado: Elsevier 2025
Materias:
Sabancaya volcano
Acceso en línea:http://hdl.handle.net/20.500.12160/2957
Aporte de:
El Abrigo - Repositorio Institucional del Servicio Meteorológico Nacional (SMN) de Servicio Meteorológico Nacional (SMN)
id I63-R169-20.500.12160-2957
record_format dspace
spelling I63-R169-20.500.12160-29572025-02-20T13:24:32Z Near-real-time multiparametric seismic and visual monitoring of explosive activity at Sabancaya volcano, Peru Centeno, Riky Gómez-Salcedo, Valeria Lazarte, Ivonne Vilca-Nina, Javier Osores, María Soledad Mayhua-Lopez, Efraín Sabancaya volcano This study presents the development of a multiparametric system that utilizes artificial intelligence techniques to identify and analyze volcanic explosions in near real-time. The study analyzed 1343 explosions recorded between 2019 and 2021, along with seismic, meteorological, and visible image data from the Sabancaya volcano. Deep learning algorithms like the U-Net convolutional neural network were used to segment and measure volcanic plumes in images, while boosting-based machine learning ensembles were used to classify seismic events related to ash plumes. The findings demonstrate that these approaches effectively handle large amounts of data generated during seismic and eruptive crises. The U-Net network achieved precise segmentation of volcanic plumes with over 98% accuracy and the ability to generalize to new data. The CatBoost classifier achieved an average accuracy of 94.5% in classifying seismic events. These approaches enable the real-time estimation of eruptive parameters without human intervention, contributing to the development of early warning systems for volcanic hazards. In conclusion, this study highlights the feasibility of using seismic signals and images to detect and characterize volcanic explosions in near real-time, making a significant contribution to the field of volcanic monitoring. 2025-02-20T10:50:05Z 2025-02-20T10:50:05Z 2024-07 Artículo Riky Centeno, Valeria Gómez-Salcedo, Ivonne Lazarte, Javier Vilca-Nina, Soledad Osores, Efraín Mayhua-Lopez, Near-real-time multiparametric seismic and visual monitoring of explosive activity at Sabancaya volcano, Peru, Journal of Volcanology and Geothermal Research, Volume 451, 2024, 108097, ISSN 0377-0273, https://doi.org/10.1016/j.jvolgeores.2024.108097. 0377-0273 http://hdl.handle.net/20.500.12160/2957 eng info:eu-repo/semantics/openAccess info:eu-repo/semantics/openAccess Elsevier
institution Servicio Meteorológico Nacional (SMN)
institution_str I-63
repository_str R-169
collection El Abrigo - Repositorio Institucional del Servicio Meteorológico Nacional (SMN)
language Inglés
topic Sabancaya volcano
spellingShingle Sabancaya volcano
Centeno, Riky
Gómez-Salcedo, Valeria
Lazarte, Ivonne
Vilca-Nina, Javier
Osores, María Soledad
Mayhua-Lopez, Efraín
Near-real-time multiparametric seismic and visual monitoring of explosive activity at Sabancaya volcano, Peru
topic_facet Sabancaya volcano
description This study presents the development of a multiparametric system that utilizes artificial intelligence techniques to identify and analyze volcanic explosions in near real-time. The study analyzed 1343 explosions recorded between 2019 and 2021, along with seismic, meteorological, and visible image data from the Sabancaya volcano. Deep learning algorithms like the U-Net convolutional neural network were used to segment and measure volcanic plumes in images, while boosting-based machine learning ensembles were used to classify seismic events related to ash plumes. The findings demonstrate that these approaches effectively handle large amounts of data generated during seismic and eruptive crises. The U-Net network achieved precise segmentation of volcanic plumes with over 98% accuracy and the ability to generalize to new data. The CatBoost classifier achieved an average accuracy of 94.5% in classifying seismic events. These approaches enable the real-time estimation of eruptive parameters without human intervention, contributing to the development of early warning systems for volcanic hazards. In conclusion, this study highlights the feasibility of using seismic signals and images to detect and characterize volcanic explosions in near real-time, making a significant contribution to the field of volcanic monitoring.
format Artículo
author Centeno, Riky
Gómez-Salcedo, Valeria
Lazarte, Ivonne
Vilca-Nina, Javier
Osores, María Soledad
Mayhua-Lopez, Efraín
author_facet Centeno, Riky
Gómez-Salcedo, Valeria
Lazarte, Ivonne
Vilca-Nina, Javier
Osores, María Soledad
Mayhua-Lopez, Efraín
author_sort Centeno, Riky
title Near-real-time multiparametric seismic and visual monitoring of explosive activity at Sabancaya volcano, Peru
title_short Near-real-time multiparametric seismic and visual monitoring of explosive activity at Sabancaya volcano, Peru
title_full Near-real-time multiparametric seismic and visual monitoring of explosive activity at Sabancaya volcano, Peru
title_fullStr Near-real-time multiparametric seismic and visual monitoring of explosive activity at Sabancaya volcano, Peru
title_full_unstemmed Near-real-time multiparametric seismic and visual monitoring of explosive activity at Sabancaya volcano, Peru
title_sort near-real-time multiparametric seismic and visual monitoring of explosive activity at sabancaya volcano, peru
publisher Elsevier
publishDate 2025
url http://hdl.handle.net/20.500.12160/2957
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