Mostrar el registro sencillo del ítem

dc.contributor.author Malfante, Marielle
dc.contributor.author Dalla Mura, Mauro
dc.contributor.author Mars, Jérôme I.
dc.contributor.author Métaxian, Jean-Philippe
dc.contributor.author Macedo Sánchez, Orlando Efraín
dc.contributor.author Inza Callupe, Lamberto Adolfo
dc.coverage.spatial Ubinas, Volcán (Moquegua, Perú)
dc.date.accessioned 2018-10-31T17:57:27Z
dc.date.available 2018-10-31T17:57:27Z
dc.date.issued 2018-09
dc.identifier.citation Malfante, 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/10.1029/2018JB015470 es_ES
dc.identifier.govdoc index-oti2018
dc.identifier.uri http://hdl.handle.net/20.500.12816/3270
dc.description.abstract The 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.format application/pdf es_ES
dc.language.iso eng es_ES
dc.publisher American Geophysical Union es_ES
dc.relation.ispartof urn:issn:0148-0227
dc.rights info:eu-repo/semantics/closedAccess es_ES
dc.subject Volcano seismic signal es_ES
dc.subject Automatic classification es_ES
dc.subject Machine learning es_ES
dc.subject Volcán Ubinas es_ES
dc.subject Volcano monitoring es_ES
dc.subject Volcanic hazards es_ES
dc.title Automatic classification of volcano seismic signatures es_ES
dc.type info:eu-repo/semantics/article es_ES
dc.subject.ocde http://purl.org/pe-repo/ocde/ford#1.05.00 es_ES
dc.subject.ocde http://purl.org/pe-repo/ocde/ford#1.05.04 es_ES
dc.subject.ocde http://purl.org/pe-repo/ocde/ford#1.05.06 es_ES
dc.subject.ocde http://purl.org/pe-repo/ocde/ford#1.05.07 es_ES
dc.identifier.journal Journal of Geophysical Research: Solid Earth es_ES
dc.description.peer-review Por pares es_ES
dc.identifier.doi https://doi.org/10.1029/2018JB015470 es_ES

Thumbnail

 Bloqueado

Colecciones

Mostrar el registro sencillo del ítem