Towards the Construction of General Deep Learning-Based Models for the Automatic Detection and Classification of Volcano-Seismic Signals

Autores:
Meneses-Muñoz, John-Jehu
Cadena-Ibarra, Oscar-Ernesto
Bolaños-González, Manuel-Ernesto
Tipo de recurso:
Article of journal
Fecha de publicación:
2026
Institución:
Universidad Pedagógica y Tecnológica de Colombia
Repositorio:
RiUPTC: Repositorio Institucional UPTC
Idioma:
eng
OAI Identifier:
oai:repositorio.uptc.edu.co:001/22980
Acceso en línea:
https://doi.org/10.19053/uptc.01211129.v35.n76.2026.20777
Palabra clave:
convolutional neural networks
earthquakes
machine learning
seismology
volcanoes
aprendizaje automático
redes neuronales convolucionales
sismología
sismos
volcanes
Rights
openAccess
License
John-Jehu Meneses-Muñoz, Oscar-Ernesto Cadena-Ibarra, Manuel-Ernesto Bolaños-González - 2026
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repository_id_str
dc.title.spa.fl_str_mv Towards the Construction of General Deep Learning-Based Models for the Automatic Detection and Classification of Volcano-Seismic Signals
title Towards the Construction of General Deep Learning-Based Models for the Automatic Detection and Classification of Volcano-Seismic Signals
spellingShingle Towards the Construction of General Deep Learning-Based Models for the Automatic Detection and Classification of Volcano-Seismic Signals
convolutional neural networks
earthquakes
machine learning
seismology
volcanoes
aprendizaje automático
redes neuronales convolucionales
sismología
sismos
volcanes
title_short Towards the Construction of General Deep Learning-Based Models for the Automatic Detection and Classification of Volcano-Seismic Signals
title_full Towards the Construction of General Deep Learning-Based Models for the Automatic Detection and Classification of Volcano-Seismic Signals
title_fullStr Towards the Construction of General Deep Learning-Based Models for the Automatic Detection and Classification of Volcano-Seismic Signals
title_full_unstemmed Towards the Construction of General Deep Learning-Based Models for the Automatic Detection and Classification of Volcano-Seismic Signals
title_sort Towards the Construction of General Deep Learning-Based Models for the Automatic Detection and Classification of Volcano-Seismic Signals
dc.creator.fl_str_mv Meneses-Muñoz, John-Jehu
Cadena-Ibarra, Oscar-Ernesto
Bolaños-González, Manuel-Ernesto
dc.contributor.author.eng.fl_str_mv Meneses-Muñoz, John-Jehu
Cadena-Ibarra, Oscar-Ernesto
Bolaños-González, Manuel-Ernesto
dc.subject.none.fl_str_mv convolutional neural networks
earthquakes
machine learning
seismology
volcanoes
aprendizaje automático
redes neuronales convolucionales
sismología
sismos
volcanes
topic convolutional neural networks
earthquakes
machine learning
seismology
volcanoes
aprendizaje automático
redes neuronales convolucionales
sismología
sismos
volcanes
publishDate 2026
dc.date.accessioned.none.fl_str_mv 2026-08-14 00:00:00
dc.date.available.none.fl_str_mv 2026-08-14 00:00:00
dc.date.issued.none.fl_str_mv 2026-08-14
dc.type.spa.fl_str_mv Artículo de revista
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dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.references.eng.fl_str_mv D. Gómez et al., Catálogo de Señales Sísmicas Volcánicas de Colombia, vol. 1. Pasto, Colombia: Instituto Colombiano de Geología y Minería, 2004. https://catalogo.sgc.gov.co/cgi-bin/koha/opac-detail.pl?biblionumber=48573
M. Malfante, M. Dalla Mura, J. I. Mars, J. P. Métaxian, O. Macedo, A. Inza, "Automatic classification of volcano seismic signatures," Journal of Geophysical Research: Solid Earth, vol. 123, no. 12, pp. 10645-10658, Dec. 2018. https://doi.org/10.1029/2018JB015470
N. Trujillo-Castrillón, C. M. Valdés-González, R. Arámbula-Mendoza, C. C. Santacoloma-Salguero, "Initial processing of volcanic seismic signals using hidden Markov models: Nevado del Huila, Colombia," Journal of Volcanology and Geothermal Research, vol. 364, pp. 107-120, Oct. 2018. https://doi.org/10.1016/j.jvolgeores.2018.09.008
R. Soto, F. Huenupan, P. Meza, M. Curilem, L. Franco, "Spectro-temporal features applied to the automatic classification of volcanic seismic events," Journal of Volcanology and Geothermal Research, vol. 358, pp. 194-206, Jun. 2018. https://doi.org/10.1016/j.jvolgeores.2018.04.025
R. Lara-Cueva, J. C. Larco, D. S. Benítez, N. Pérez, F. Grijalva, M. Ruiz, "On finding possible frequencies for recognizing microearthquakes at Cotopaxi volcano: A machine learning based approach," Journal of Volcanology and Geothermal Research, vol. 407, e107092, Dec. 2020. https://doi.org/10.1016/j.jvolgeores.2020.107092
Y. Maeda, Y. Yamanaka, T. Ito, S. Horikawa, "Machine-learning-based detection of volcano seismicity using the spatial pattern of amplitudes," Geophysical Journal International, vol. 225, no. 1, pp. 416-444, Apr. 2021. https://doi.org/10.1093/gji/ggaa593
A. A. T. Peixoto et al., "Tensor-based learning framework for automatic multichannel volcano-seismic classification," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 4517-4529, Apr. 2021. https://doi.org/10.1109/JSTARS.2021.3074058
A. Falcin et al., "A machine-learning approach for automatic classification of volcanic seismicity at La Soufrière Volcano, Guadeloupe," Journal of Volcanology and Geothermal Research, vol. 411, e107151, Feb. 2021. https://doi.org/10.1016/j.jvolgeores.2020.107151
V. L. Martínez et al., "Advanced signal recognition methods applied to seismo-volcanic events from Planchon Peteroa Volcanic Complex: Deep neural network classifier," Journal of South American Earth Sciences, vol. 107, e103115, Apr. 2021. https://doi.org/10.1016/j.jsames.2020.103115
M. Titos, L. García, M. Kowsari, and C. Benítez, "Toward knowledge extraction in classification of volcano-seismic events: Visualizing hidden states in recurrent neural networks," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 2311-2325, Feb. 2022. https://doi.org/10.1109/JSTARS.2022.3155967
F. Lara, R. Lara-Cueva, J. C. Larco, E. V. Carrera, R. León, "A deep learning approach for automatic recognition of seismo-volcanic events at the Cotopaxi volcano," Journal of Volcanology and Geothermal Research, vol. 409, e107142, Jan. 2021. https://doi.org/10.1016/j.jvolgeores.2020.107142
G. F. Manley et al., "A deep active learning approach to the automatic classification of volcano-seismic events," Frontiers in Earth Science, vol. 10, e807926, Mar. 2022. https://doi.org/10.3389/feart.2022.807926
D. Rincon-Yanez, E. De Lauro, S. Petrosino, S. Senatore, M. Falanga, "Identifying the fingerprint of a volcano in the background seismic noise from machine learning-based approach," Applied Sciences, vol. 12, no. 14, e6835, Jul. 2022. https://doi.org/10.3390/app12146835
P. Salazar, F. Yupanqui, C. Meneses, S. Layana, G. Yáñez, "Multi-station automatic classification of seismic signatures from the Lascar volcano database," Natural Hazards and Earth System Sciences, vol. 23, no. 2, pp. 991-1006, Mar. 2023. https://doi.org/10.5194/nhess-23-991-2023
M. Nakano, D. Sugiyama, "Discriminating seismic events using 1D and 2D CNNs: Applications to volcanic and tectonic datasets," Earth, Planets and Space, vol. 74, no. 1, e134, Sep. 2022. https://doi.org/10.1186/s40623-022-01696-1
G. Cortés, R. Carniel, P. Lesage, M. Á. Mendoza, I. Della Lucia, "Practical volcano-independent recognition of seismic events: VULCAN.ears project," Frontiers in Earth Science, vol. 8, e616676, Jan. 2021. https://doi.org/10.3389/feart.2020.616676
N. Pérez, D. Benítez, F. Grijalva, R. Lara-Cueva, M. Ruiz, J. Aguilar, "ESeismic: Towards an Ecuadorian volcano seismic repository," Journal of Volcanology and Geothermal Research, vol. 396, e106855, May 2020. https://doi.org/10.1016/j.jvolgeores.2020.106855
M. Tan, Q. V. Le, "EfficientNet: Rethinking model scaling for convolutional neural networks," in Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, CA, USA, Jun. 2019, pp. 6105-6114.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, "ImageNet: A large-scale hierarchical image database," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, USA, Jun. 2009, pp. 248-255. https://doi.org/10.1109/CVPR.2009.5206848
F. Zhuang et al., "A comprehensive survey on transfer learning," Proceedings of the IEEE, vol. 109, no. 1, pp. 43-76, Jan. 2021. https://doi.org/10.1109/JPROC.2020.3004555
J. Yosinski, J. Clune, Y. Bengio, H. Lipson, "How transferable are features in deep neural networks?," in Proceedings of Advances in Neural Information Processing Systems 27 (NIPS 2014), Montreal, QC, Canada, Dec. 2014, pp. 3320-3328.
R. Borja-Robalino, A. Monleón-Getino, J. Rodellar, "Estandarización de métricas de rendimiento para clasificadores machine y deep learning," RISTI – Revista Ibérica de Sistemas e Tecnologias de Informação, no. E30, pp. 172-184, Jun. 2020.
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dc.relation.citationissue.eng.fl_str_mv 76
dc.relation.citationstartpage.none.fl_str_mv e20777
dc.relation.citationvolume.eng.fl_str_mv 35
dc.relation.ispartofjournal.eng.fl_str_mv Revista Facultad de Ingeniería
dc.relation.bitstream.none.fl_str_mv https://revistas.uptc.edu.co/index.php/ingenieria/article/download/20777/17596
dc.relation.citationedition.eng.fl_str_mv Núm. 76 , Año 2026 : April-June 2026 (Continuos Publication)
dc.rights.eng.fl_str_mv John-Jehu Meneses-Muñoz, Oscar-Ernesto Cadena-Ibarra, Manuel-Ernesto Bolaños-González - 2026
dc.rights.uri.eng.fl_str_mv https://creativecommons.org/licenses/by/4.0
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dc.rights.creativecommons.eng.fl_str_mv This work is licensed under a Creative Commons Attribution 4.0 International License.
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rights_invalid_str_mv John-Jehu Meneses-Muñoz, Oscar-Ernesto Cadena-Ibarra, Manuel-Ernesto Bolaños-González - 2026
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spelling Meneses-Muñoz, John-JehuCadena-Ibarra, Oscar-ErnestoBolaños-González, Manuel-Ernesto2026-08-14 00:00:002026-08-14 00:00:002026-08-140121-112910.19053/uptc.01211129.v35.n76.2026.207772357-5328https://doi.org/10.19053/uptc.01211129.v35.n76.2026.20777application/pdfengUniversidad Pedagógica y Tecnológica de ColombiaJohn-Jehu Meneses-Muñoz, Oscar-Ernesto Cadena-Ibarra, Manuel-Ernesto Bolaños-González - 2026https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/ingenieria/article/view/20777convolutional neural networksearthquakesmachine learningseismologyvolcanoesaprendizaje automáticoredes neuronales convolucionalessismologíasismosvolcanesTowards the Construction of General Deep Learning-Based Models for the Automatic Detection and Classification of Volcano-Seismic SignalsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/coar/version/c_970fb48d4fbd8a85D. Gómez et al., Catálogo de Señales Sísmicas Volcánicas de Colombia, vol. 1. Pasto, Colombia: Instituto Colombiano de Geología y Minería, 2004. https://catalogo.sgc.gov.co/cgi-bin/koha/opac-detail.pl?biblionumber=48573M. Malfante, M. Dalla Mura, J. I. Mars, J. P. Métaxian, O. Macedo, A. Inza, "Automatic classification of volcano seismic signatures," Journal of Geophysical Research: Solid Earth, vol. 123, no. 12, pp. 10645-10658, Dec. 2018. https://doi.org/10.1029/2018JB015470N. Trujillo-Castrillón, C. M. Valdés-González, R. Arámbula-Mendoza, C. C. Santacoloma-Salguero, "Initial processing of volcanic seismic signals using hidden Markov models: Nevado del Huila, Colombia," Journal of Volcanology and Geothermal Research, vol. 364, pp. 107-120, Oct. 2018. https://doi.org/10.1016/j.jvolgeores.2018.09.008R. Soto, F. Huenupan, P. Meza, M. Curilem, L. Franco, "Spectro-temporal features applied to the automatic classification of volcanic seismic events," Journal of Volcanology and Geothermal Research, vol. 358, pp. 194-206, Jun. 2018. https://doi.org/10.1016/j.jvolgeores.2018.04.025R. Lara-Cueva, J. C. Larco, D. S. Benítez, N. Pérez, F. Grijalva, M. Ruiz, "On finding possible frequencies for recognizing microearthquakes at Cotopaxi volcano: A machine learning based approach," Journal of Volcanology and Geothermal Research, vol. 407, e107092, Dec. 2020. https://doi.org/10.1016/j.jvolgeores.2020.107092Y. Maeda, Y. Yamanaka, T. Ito, S. Horikawa, "Machine-learning-based detection of volcano seismicity using the spatial pattern of amplitudes," Geophysical Journal International, vol. 225, no. 1, pp. 416-444, Apr. 2021. https://doi.org/10.1093/gji/ggaa593A. A. T. Peixoto et al., "Tensor-based learning framework for automatic multichannel volcano-seismic classification," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 4517-4529, Apr. 2021. https://doi.org/10.1109/JSTARS.2021.3074058A. Falcin et al., "A machine-learning approach for automatic classification of volcanic seismicity at La Soufrière Volcano, Guadeloupe," Journal of Volcanology and Geothermal Research, vol. 411, e107151, Feb. 2021. https://doi.org/10.1016/j.jvolgeores.2020.107151V. L. Martínez et al., "Advanced signal recognition methods applied to seismo-volcanic events from Planchon Peteroa Volcanic Complex: Deep neural network classifier," Journal of South American Earth Sciences, vol. 107, e103115, Apr. 2021. https://doi.org/10.1016/j.jsames.2020.103115M. Titos, L. García, M. Kowsari, and C. Benítez, "Toward knowledge extraction in classification of volcano-seismic events: Visualizing hidden states in recurrent neural networks," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 2311-2325, Feb. 2022. https://doi.org/10.1109/JSTARS.2022.3155967F. Lara, R. Lara-Cueva, J. C. Larco, E. V. Carrera, R. León, "A deep learning approach for automatic recognition of seismo-volcanic events at the Cotopaxi volcano," Journal of Volcanology and Geothermal Research, vol. 409, e107142, Jan. 2021. https://doi.org/10.1016/j.jvolgeores.2020.107142G. F. Manley et al., "A deep active learning approach to the automatic classification of volcano-seismic events," Frontiers in Earth Science, vol. 10, e807926, Mar. 2022. https://doi.org/10.3389/feart.2022.807926D. Rincon-Yanez, E. De Lauro, S. Petrosino, S. Senatore, M. Falanga, "Identifying the fingerprint of a volcano in the background seismic noise from machine learning-based approach," Applied Sciences, vol. 12, no. 14, e6835, Jul. 2022. https://doi.org/10.3390/app12146835P. Salazar, F. Yupanqui, C. Meneses, S. Layana, G. Yáñez, "Multi-station automatic classification of seismic signatures from the Lascar volcano database," Natural Hazards and Earth System Sciences, vol. 23, no. 2, pp. 991-1006, Mar. 2023. https://doi.org/10.5194/nhess-23-991-2023M. Nakano, D. Sugiyama, "Discriminating seismic events using 1D and 2D CNNs: Applications to volcanic and tectonic datasets," Earth, Planets and Space, vol. 74, no. 1, e134, Sep. 2022. https://doi.org/10.1186/s40623-022-01696-1G. Cortés, R. Carniel, P. Lesage, M. Á. Mendoza, I. Della Lucia, "Practical volcano-independent recognition of seismic events: VULCAN.ears project," Frontiers in Earth Science, vol. 8, e616676, Jan. 2021. https://doi.org/10.3389/feart.2020.616676N. Pérez, D. Benítez, F. Grijalva, R. Lara-Cueva, M. Ruiz, J. Aguilar, "ESeismic: Towards an Ecuadorian volcano seismic repository," Journal of Volcanology and Geothermal Research, vol. 396, e106855, May 2020. https://doi.org/10.1016/j.jvolgeores.2020.106855M. Tan, Q. V. Le, "EfficientNet: Rethinking model scaling for convolutional neural networks," in Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, CA, USA, Jun. 2019, pp. 6105-6114.J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, "ImageNet: A large-scale hierarchical image database," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, USA, Jun. 2009, pp. 248-255. https://doi.org/10.1109/CVPR.2009.5206848F. Zhuang et al., "A comprehensive survey on transfer learning," Proceedings of the IEEE, vol. 109, no. 1, pp. 43-76, Jan. 2021. https://doi.org/10.1109/JPROC.2020.3004555J. Yosinski, J. Clune, Y. Bengio, H. Lipson, "How transferable are features in deep neural networks?," in Proceedings of Advances in Neural Information Processing Systems 27 (NIPS 2014), Montreal, QC, Canada, Dec. 2014, pp. 3320-3328.R. Borja-Robalino, A. Monleón-Getino, J. Rodellar, "Estandarización de métricas de rendimiento para clasificadores machine y deep learning," RISTI – Revista Ibérica de Sistemas e Tecnologias de Informação, no. E30, pp. 172-184, Jun. 2020.e2077776e2077735Revista Facultad de Ingenieríahttps://revistas.uptc.edu.co/index.php/ingenieria/article/download/20777/17596Núm. 76 , Año 2026 : April-June 2026 (Continuos Publication)PublicationOREORE.xmltext/xml2810https://repositorio.uptc.edu.co/bitstreams/d402821d-f9fd-4479-8112-4adfde4a0ea3/downloadf1ca51e9cd648be06cc18bbc2d48400cMD51falseAnonymousREAD001/22980oai:repositorio.uptc.edu.co:001/229802026-11-04 05:45:11.72https://creativecommons.org/licenses/by/4.0metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCbdigital@metabiblioteca.com

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