Automatic classification of volcano seismic signatures

Simple item page
dc.contributor.authorMalfante, Marielle
dc.contributor.authorDalla Mura, Mauro
dc.contributor.authorMars, Jérôme I.
dc.contributor.authorMétaxian, Jean-Philippe
dc.contributor.authorMacedo Sánchez, Orlando Efraín
dc.contributor.authorInza Callupe, Lamberto Adolfo
dc.coverage.spatialUbinas, Volcán (Moquegua, Perú)
dc.date.accessioned2018-10-31T17:57:27Z
dc.date.available2018-10-31T17:57:27Z
dc.date.issued2018-12
dc.description.abstractThe prediction of volcanic eruptions and the evaluation of associated risks remain a timely and unresolved issue. This paper presents a method to automatically classify seismic events linked to volcanic activity. As increased seismic activity is an indicator of volcanic unrest, automatic classification of volcano‐seismic events is of major interest for volcano monitoring. The proposed architecture is based on supervised classification, whereby a prediction model is built from an extensive dataset of labeled observations. Relevant events should then be detected. Three steps are involved in the building of the prediction model: (i) signals pre‐processing ; (ii) represention of the signals in the feature space; (iii) use of an automatic classifier to train the model. Our main contribution lies in the feature space where the seismic observations are represented by 102 features gathered from both acoustic and seismic fields. Ideally, observations are separable in the feature space, depending on their class. The architecture is tested on 109,609 seismic events that were recorded between June 2006 and September 2011 at Ubinas Volcano, Peru. Six main classes of signals are considered: long period events, volcanic tremors, volcano‐tectonic events, explosions, hybrid events, and tornillos. Our model reaches 93.5% ± 0.50% accuracy, thereby validating the presented architecture and the features used. Furthermore, we illustrate the limited influence of the learning algorithm used (i.e., random forest, support vector machines) by showing that the results remain accurate regardless of the algorithm selected for the training stage. The model is then used to analyze 6 years of data.es_ES
dc.description.peer-reviewPor pareses_ES
dc.formatapplication/pdfes_ES
dc.identifier.citationMalfante, M., Dalla, M., Mars, J. I., Métaxian, J.-P., Macedo, O., & Inza, A. (2018). Automatic classification of volcano seismic signatures.==$Journal of Geophysical Research: Solid Earth, 123,$==10,645– 10,658. https://doi.org/10.1029/2018JB015470es_ES
dc.identifier.doihttps://doi.org/10.1029/2018JB015470es_ES
dc.identifier.govdocindex-oti2018
dc.identifier.journalJournal of Geophysical Research: Solid Earthes_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12816/3270
dc.language.isoenges_ES
dc.publisherAmerican Geophysical Uniones_ES
dc.relation.ispartofurn:issn:0148-0227
dc.rightsinfo:eu-repo/semantics/closedAccesses_ES
dc.subjectVolcano seismic signales_ES
dc.subjectAutomatic classificationes_ES
dc.subjectMachine learninges_ES
dc.subjectVolcán Ubinases_ES
dc.subjectVolcano monitoringes_ES
dc.subjectVolcanic hazardses_ES
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#1.05.00es_ES
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#1.05.04es_ES
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#1.05.06es_ES
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#1.05.07es_ES
dc.titleAutomatic classification of volcano seismic signatureses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Malfante_et_al_2018_JGR_Solid_Earth.pdf
Size:
2.26 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Artículos Científicos