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Tracking volcanic explosions using Shannon entropy at Volcán de Colima
The main objective of this work is to show that Shannon Entropy (SE) calculated on continuous seismic signals can be used in a volcanic eruption monitoring system. We analysed three years of volcanic activity of Volcán de Colima, México, recorded between January 2015 and May 2017. This period includ...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10276871/ https://www.ncbi.nlm.nih.gov/pubmed/37330531 http://dx.doi.org/10.1038/s41598-023-36964-x |
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author | Rey-Devesa, Pablo Prudencio, Janire Benítez, Carmen Bretón, Mauricio Plasencia, Imelda León, Zoraida Ortigosa, Félix Gutiérrez, Ligdamis Arámbula-Mendoza, Raúl Ibáñez, Jesús M. |
author_facet | Rey-Devesa, Pablo Prudencio, Janire Benítez, Carmen Bretón, Mauricio Plasencia, Imelda León, Zoraida Ortigosa, Félix Gutiérrez, Ligdamis Arámbula-Mendoza, Raúl Ibáñez, Jesús M. |
author_sort | Rey-Devesa, Pablo |
collection | PubMed |
description | The main objective of this work is to show that Shannon Entropy (SE) calculated on continuous seismic signals can be used in a volcanic eruption monitoring system. We analysed three years of volcanic activity of Volcán de Colima, México, recorded between January 2015 and May 2017. This period includes two large explosions, with pyroclastic and lava flows, and intense activity of less energetic explosion, culminating with a period of quiescence. In order to confirm the success of our results, we used images of the Visual Monitoring system of Colima Volcano Observatory. Another of the objectives of this work is to show how the decrease in SE values can be used to track minor explosive activity, helping Machine Learning algorithms to work more efficiently in the complex problem of distinguishing the explosion signals in the seismograms. We show that the two big eruptions selected were forecasted successfully (6 and 2 days respectively) using the decay of SE. We conclude that SE could be used as a complementary tool in seismic volcano monitoring, showing its successful behaviour prior to energetic eruptions, giving time enough to alert the population and prepare for the consequences of an imminent and well predicted moment of the eruption. |
format | Online Article Text |
id | pubmed-10276871 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-102768712023-06-19 Tracking volcanic explosions using Shannon entropy at Volcán de Colima Rey-Devesa, Pablo Prudencio, Janire Benítez, Carmen Bretón, Mauricio Plasencia, Imelda León, Zoraida Ortigosa, Félix Gutiérrez, Ligdamis Arámbula-Mendoza, Raúl Ibáñez, Jesús M. Sci Rep Article The main objective of this work is to show that Shannon Entropy (SE) calculated on continuous seismic signals can be used in a volcanic eruption monitoring system. We analysed three years of volcanic activity of Volcán de Colima, México, recorded between January 2015 and May 2017. This period includes two large explosions, with pyroclastic and lava flows, and intense activity of less energetic explosion, culminating with a period of quiescence. In order to confirm the success of our results, we used images of the Visual Monitoring system of Colima Volcano Observatory. Another of the objectives of this work is to show how the decrease in SE values can be used to track minor explosive activity, helping Machine Learning algorithms to work more efficiently in the complex problem of distinguishing the explosion signals in the seismograms. We show that the two big eruptions selected were forecasted successfully (6 and 2 days respectively) using the decay of SE. We conclude that SE could be used as a complementary tool in seismic volcano monitoring, showing its successful behaviour prior to energetic eruptions, giving time enough to alert the population and prepare for the consequences of an imminent and well predicted moment of the eruption. Nature Publishing Group UK 2023-06-17 /pmc/articles/PMC10276871/ /pubmed/37330531 http://dx.doi.org/10.1038/s41598-023-36964-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Rey-Devesa, Pablo Prudencio, Janire Benítez, Carmen Bretón, Mauricio Plasencia, Imelda León, Zoraida Ortigosa, Félix Gutiérrez, Ligdamis Arámbula-Mendoza, Raúl Ibáñez, Jesús M. Tracking volcanic explosions using Shannon entropy at Volcán de Colima |
title | Tracking volcanic explosions using Shannon entropy at Volcán de Colima |
title_full | Tracking volcanic explosions using Shannon entropy at Volcán de Colima |
title_fullStr | Tracking volcanic explosions using Shannon entropy at Volcán de Colima |
title_full_unstemmed | Tracking volcanic explosions using Shannon entropy at Volcán de Colima |
title_short | Tracking volcanic explosions using Shannon entropy at Volcán de Colima |
title_sort | tracking volcanic explosions using shannon entropy at volcán de colima |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10276871/ https://www.ncbi.nlm.nih.gov/pubmed/37330531 http://dx.doi.org/10.1038/s41598-023-36964-x |
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