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Malfante M., Dalla Mura M., Mars J. I., Métaxian Jean-Philippe, Macedo O., Inza A. (2018). Automatic classification of volcano seismic signatures. Journal of Geophysical Research : Solid Earth, 123 (12), 10645-10658. ISSN 2169-9313

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Lien direct chez l'éditeur doi:10.1029/2018jb015470

Automatic classification of volcano seismic signatures
Année de publication2018
Type de documentArticle référencé dans le Web of Science WOS:000455996900017
AuteursMalfante M., Dalla Mura M., Mars J. I., Métaxian Jean-Philippe, Macedo O., Inza A.
SourceJournal of Geophysical Research : Solid Earth, 2018, 123 (12), p. 10645-10658. ISSN 2169-9313
RésuméThe 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 data set of labeled observations. Relevant events should then be detected. Three steps are involved in the building of the prediction model: (i) signals preprocessing, (ii) representation of the signals in the feature space, and (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 and 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 6years of data.
Plan de classementGéophysique interne [066] ; Sciences fondamentales / Techniques d'analyse et de recherche [020]
LocalisationFonds IRD [F B010074919]
Identifiant IRDfdi:010074919
Lien permanenthttp://www.documentation.ird.fr/hor/fdi:010074919

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