Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation

Autores
Mauad Sosa, Yair; Molina, Romina Soledad; Spagnotto, Silvana Liz; Fernandez Melchor, Ivan; Nuñez Manquez, Alejandro Enrique; Crespo, Maria Liz; Ramponi, Giovanni; Petrino, Ricardo
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This study focuses on volcano seismic event detection using machine learning, leveraging the advantages of software/hardware co-design for system on chip (SoC) based on field programmable gate array (FPGA) devices. The case study was the Copahue Volcano, an active stratovolcano located on the border between Argentina and Chile. Volcano seismic event processing and detection were integrated into a PYNQ-based implementation by using a low-end SoC-FPGA device. We also provide insights into integrating an SoC-FPGA in the acquisition node, which can be valuable in scenarios where stations are deployed solely for data collection and holds the potential for developing an early alert system.
Fil: Mauad Sosa, Yair. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Electrónica; Argentina
Fil: Molina, Romina Soledad. Università degli Studi di Trieste; Italia
Fil: Spagnotto, Silvana Liz. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Geología; Argentina. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina
Fil: Fernandez Melchor, Ivan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación en Paleobiología y Geología; Argentina
Fil: Nuñez Manquez, Alejandro Enrique. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Electrónica; Argentina
Fil: Crespo, Maria Liz. Università degli Studi di Trieste; Italia
Fil: Ramponi, Giovanni. Università degli Studi di Trieste; Italia
Fil: Petrino, Ricardo. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Electrónica; Argentina
Materia
Copahue Volcano
Machine Learning
Soc FPGA
seismic events
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/231080
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/231080
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge ImplementationMauad Sosa, YairMolina, Romina SoledadSpagnotto, Silvana LizFernandez Melchor, IvanNuñez Manquez, Alejandro EnriqueCrespo, Maria LizRamponi, GiovanniPetrino, RicardoCopahue VolcanoMachine LearningSoc FPGAseismic eventshttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2This study focuses on volcano seismic event detection using machine learning, leveraging the advantages of software/hardware co-design for system on chip (SoC) based on field programmable gate array (FPGA) devices. The case study was the Copahue Volcano, an active stratovolcano located on the border between Argentina and Chile. Volcano seismic event processing and detection were integrated into a PYNQ-based implementation by using a low-end SoC-FPGA device. We also provide insights into integrating an SoC-FPGA in the acquisition node, which can be valuable in scenarios where stations are deployed solely for data collection and holds the potential for developing an early alert system.Fil: Mauad Sosa, Yair. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Electrónica; ArgentinaFil: Molina, Romina Soledad. Università degli Studi di Trieste; ItaliaFil: Spagnotto, Silvana Liz. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Geología; Argentina. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; ArgentinaFil: Fernandez Melchor, Ivan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación en Paleobiología y Geología; ArgentinaFil: Nuñez Manquez, Alejandro Enrique. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Electrónica; ArgentinaFil: Crespo, Maria Liz. Università degli Studi di Trieste; ItaliaFil: Ramponi, Giovanni. Università degli Studi di Trieste; ItaliaFil: Petrino, Ricardo. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Electrónica; ArgentinaMDPI2024-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/231080Mauad Sosa, Yair; Molina, Romina Soledad; Spagnotto, Silvana Liz; Fernandez Melchor, Ivan; Nuñez Manquez, Alejandro Enrique; et al.; Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation; MDPI; Electronics; 13; 3; 2-2024; 1-192079-9292CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2079-9292/13/3/622info:eu-repo/semantics/altIdentifier/doi/10.3390/electronics13030622info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-26T08:44:56Zoai:ri.conicet.gov.ar:11336/231080instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-11-26 08:44:56.354CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation
title Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation
spellingShingle Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation
Mauad Sosa, Yair
Copahue Volcano
Machine Learning
Soc FPGA
seismic events
title_short Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation
title_full Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation
title_fullStr Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation
title_full_unstemmed Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation
title_sort Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation
dc.creator.none.fl_str_mv Mauad Sosa, Yair
Molina, Romina Soledad
Spagnotto, Silvana Liz
Fernandez Melchor, Ivan
Nuñez Manquez, Alejandro Enrique
Crespo, Maria Liz
Ramponi, Giovanni
Petrino, Ricardo
author Mauad Sosa, Yair
author_facet Mauad Sosa, Yair
Molina, Romina Soledad
Spagnotto, Silvana Liz
Fernandez Melchor, Ivan
Nuñez Manquez, Alejandro Enrique
Crespo, Maria Liz
Ramponi, Giovanni
Petrino, Ricardo
author_role author
author2 Molina, Romina Soledad
Spagnotto, Silvana Liz
Fernandez Melchor, Ivan
Nuñez Manquez, Alejandro Enrique
Crespo, Maria Liz
Ramponi, Giovanni
Petrino, Ricardo
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Copahue Volcano
Machine Learning
Soc FPGA
seismic events
topic Copahue Volcano
Machine Learning
Soc FPGA
seismic events
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This study focuses on volcano seismic event detection using machine learning, leveraging the advantages of software/hardware co-design for system on chip (SoC) based on field programmable gate array (FPGA) devices. The case study was the Copahue Volcano, an active stratovolcano located on the border between Argentina and Chile. Volcano seismic event processing and detection were integrated into a PYNQ-based implementation by using a low-end SoC-FPGA device. We also provide insights into integrating an SoC-FPGA in the acquisition node, which can be valuable in scenarios where stations are deployed solely for data collection and holds the potential for developing an early alert system.
Fil: Mauad Sosa, Yair. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Electrónica; Argentina
Fil: Molina, Romina Soledad. Università degli Studi di Trieste; Italia
Fil: Spagnotto, Silvana Liz. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Geología; Argentina. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina
Fil: Fernandez Melchor, Ivan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación en Paleobiología y Geología; Argentina
Fil: Nuñez Manquez, Alejandro Enrique. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Electrónica; Argentina
Fil: Crespo, Maria Liz. Università degli Studi di Trieste; Italia
Fil: Ramponi, Giovanni. Università degli Studi di Trieste; Italia
Fil: Petrino, Ricardo. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Electrónica; Argentina
description This study focuses on volcano seismic event detection using machine learning, leveraging the advantages of software/hardware co-design for system on chip (SoC) based on field programmable gate array (FPGA) devices. The case study was the Copahue Volcano, an active stratovolcano located on the border between Argentina and Chile. Volcano seismic event processing and detection were integrated into a PYNQ-based implementation by using a low-end SoC-FPGA device. We also provide insights into integrating an SoC-FPGA in the acquisition node, which can be valuable in scenarios where stations are deployed solely for data collection and holds the potential for developing an early alert system.
publishDate 2024
dc.date.none.fl_str_mv 2024-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/231080
Mauad Sosa, Yair; Molina, Romina Soledad; Spagnotto, Silvana Liz; Fernandez Melchor, Ivan; Nuñez Manquez, Alejandro Enrique; et al.; Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation; MDPI; Electronics; 13; 3; 2-2024; 1-19
2079-9292
CONICET Digital
CONICET
url http://hdl.handle.net/11336/231080
identifier_str_mv Mauad Sosa, Yair; Molina, Romina Soledad; Spagnotto, Silvana Liz; Fernandez Melchor, Ivan; Nuñez Manquez, Alejandro Enrique; et al.; Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation; MDPI; Electronics; 13; 3; 2-2024; 1-19
2079-9292
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2079-9292/13/3/622
info:eu-repo/semantics/altIdentifier/doi/10.3390/electronics13030622
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1849872427155193856
score 13.011256

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