MISC

2018年12月

Automatic Classification of Volcano Seismic Signatures

Journal of Geophysical Research: Solid Earth
  • Marielle Malfante
  • ,
  • Mauro Dalla Mura
  • ,
  • Jerome I. Mars
  • ,
  • Jean Philippe Métaxian
  • ,
  • Orlando Macedo
  • ,
  • Adolfo Inza

123
12
開始ページ
10,645
終了ページ
10,658
DOI
10.1029/2018JB015470
出版者・発行元
American Geophysical Union ({AGU})

©2018. American Geophysical Union. All Rights Reserved. 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.

リンク情報
DOI
https://doi.org/10.1029/2018JB015470
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85057739147&origin=inward
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85057739147&origin=inward
ID情報
  • DOI : 10.1029/2018JB015470
  • ISSN : 2169-9313
  • eISSN : 2169-9356
  • ORCIDのPut Code : 51324521
  • SCOPUS ID : 85057739147

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