Automatic Classification of Volcano Seismic Signatures

Marielle Malfante, Mauro Dalla Mura, Jerome I. Mars, Jean Philippe Métaxian, Orlando Efrain Macedo Sánchez, Adolfo Inza

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

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 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 6 years of data.

Original languageEnglish
Pages (from-to)10,645-10,658
JournalJournal of Geophysical Research: Solid Earth
Volume123
Issue number12
DOIs
StatePublished - Dec 2018

Bibliographical note

Funding Information:
Ubinas Volcano is an andesitic stratovolcano in southern Peru, (16∘ 22′S, 70∘ 54′W; altitude, 5,672 m). It is considered to be the most active vol-cano in Peru, and it is closely monitored by the Instituto Geofísico del Perú (IGP). After nearly 40 years of quiescence, Ubinas Volcano erupted in 2006. Three eruptions have occurred since 2006, from 2006 to 2011, from 2013 to 2014, and in 2016. Ubinas Volcano has been monitored seismically by the IGP since 2006 (Macedo et al., 2009), with the cooperation of the VOLUME project (funded by the European Commission 6th Framework Program) and the Institut de Recherche pour le Developpement (France). The first permanent telemetered station (i.e., UBIW) was equipped with a short-period vertical 1-Hz sensor that was installed in May 2006 on the northwest flank of Ubinas Volcano (Macedo et al., 2009). Three additional stations were added in 2007 (i.e., UBIN, UBIE, and UBIS). UBIN was equipped with a broadband vertical sensor, and the other stations had short-period sensors. In addition, UBIN and UBIS were equipped with biaxial tiltmeters with 0.1-μrad resolution (Inza et al., 2014). These four stations have been working permanently since 2007 (Figure 1). The data are recorded continuously with a sampling rate of 100 Hz, and they are then transmitted in real time to Cayma Volcanological Observatory in Arequipa (Peru). In this paper, our analysis is based on seismic data from the vertical component of UBIW station.

Publisher Copyright:
©2018. American Geophysical Union. All Rights Reserved.

Keywords

  • Ubinas Volcano
  • automatic classification
  • machine learning
  • volcanic hazards
  • volcano monitoring
  • volcano seismic signal

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