Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

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dc.contributor.authorEspinoza Lara, Pablo Eduardo
dc.contributor.authorRolim Fernandes, Carlos Alexandre
dc.contributor.authorInza Callupe, Lamberto Adolfo
dc.contributor.authorMars, Jérôme I.
dc.contributor.authorMétaxian, Jean-Philippe
dc.contributor.authorDalla Mura, Mauro
dc.contributor.authorMalfante, Marielle
dc.coverage.spatialUbinas, Volcán (Moquegua, Perú)
dc.date.accessioned2020-11-23T17:33:24Z
dc.date.available2020-11-23T17:33:24Z
dc.date.issued2020-03-27
dc.description.abstractThis article proposes the design of an automatic classifier using the empirical mode decomposition (EMD) along with machine learning techniques for identifying the five most important types of events of the Ubinas volcano, the most active volcano in Peru. The proposed method uses attributes from temporal, spectral, and cepstral domains, extracted from the EMD of the signals, as well as a set of preprocessing and instrument correction techniques. Due to the fact that multichannel sensors are currently being installed in seismic networks worldwide, the proposed approach uses a multichannel sensor to perform the classification, contrary to the usual approach of the literature of using a single channel. The presented method is scalable to use data from multiple stations with one or more channels. The principal component analysis method is applied to reduce the dimensionality of the feature vector and the supervised classification is carried out by means of several machine learning algorithms, the support vector machine providing the best results. The presented investigation was tested with a large database that has a considerable number of explosion events, measured at the Ubinas volcano, located in Arequipa, Peru. The proposed classification system achieved a success rate of more than 90%.es_ES
dc.description.peer-reviewPor pareses_ES
dc.formatapplication/pdfes_ES
dc.identifier.citationEspinoza, P. E., Rolim, C. A., Inza, A., Mars, J. I., Métaxian, J.-P., Dalla, M., & Malfante, M. (2020). Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD.==$IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13,$==1322-1331. https://doi.org/10.1109/JSTARS.2020.2982714es_ES
dc.identifier.doihttps://doi.org/10.1109/JSTARS.2020.2982714es_ES
dc.identifier.govdocindex-oti2018
dc.identifier.journalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensinges_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12816/4884
dc.language.isoenges_ES
dc.publisherInstitute of Electrical and Electronics Engineerses_ES
dc.relation.ispartofurn:issn:1939-1404
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttps://creativecommons.org/licences/by/4.0/es_ES
dc.subjectArtificial intelligencees_ES
dc.subjectEmpirical mode decompositiones_ES
dc.subjectDeconvolutiones_ES
dc.subjectTime domain analysises_ES
dc.subjectSpectral domain analysises_ES
dc.subjectCepstral analysises_ES
dc.subjectSeismic signal processinges_ES
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#1.05.07es_ES
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#1.05.00es_ES
dc.titleAutomatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMDes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES

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