Malfante, MarielleDalla Mura, MauroMars, Jérôme I.Métaxian, Jean-PhilippeMacedo Sánchez, Orlando EfraínInza Callupe, Lamberto Adolfo2018-10-312018-10-312018-12Malfante, 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/10.1029/2018JB015470index-oti2018http://hdl.handle.net/20.500.12816/3270The 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.application/pdfenginfo:eu-repo/semantics/closedAccessVolcano seismic signalAutomatic classificationMachine learningVolcán UbinasVolcano monitoringVolcanic hazardsinfo:eu-repo/semantics/articlehttp://purl.org/pe-repo/ocde/ford#1.05.00http://purl.org/pe-repo/ocde/ford#1.05.04http://purl.org/pe-repo/ocde/ford#1.05.06http://purl.org/pe-repo/ocde/ford#1.05.07Journal of Geophysical Research: Solid Earthhttps://doi.org/10.1029/2018JB015470