論文

本文へのリンクあり
2020年

Automatic multichannel volcano-seismic classification using machine learning and EMD

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • Pablo Eduardo Espinoza Lara
  • ,
  • Carlos Alexandre Rolim Fernandes
  • ,
  • Adolfo Inza
  • ,
  • Jerome I. Mars
  • ,
  • Jean Philippe Metaxian
  • ,
  • Mauro Dalla Mura
  • ,
  • Marielle Malfante

13
開始ページ
1322
終了ページ
1331
記述言語
掲載種別
研究論文(学術雑誌)
DOI
10.1109/JSTARS.2020.2982714

© 2008-2012 IEEE. This article proposes the design of an automatic classifier using the empirical mode decomposition (EMD) along with machine learning techniques for identifying the five most important types of events of the Ubinas volcano, the most active volcano in Peru. The proposed method uses attributes from temporal, spectral, and cepstral domains, extracted from the EMD of the signals, as well as a set of preprocessing and instrument correction techniques. Due to the fact that multichannel sensors are currently being installed in seismic networks worldwide, the proposed approach uses a multichannel sensor to perform the classification, contrary to the usual approach of the literature of using a single channel. The presented method is scalable to use data from multiple stations with one or more channels. The principal component analysis method is applied to reduce the dimensionality of the feature vector and the supervised classification is carried out by means of several machine learning algorithms, the support vector machine providing the best results. The presented investigation was tested with a large database that has a considerable number of explosion events, measured at the Ubinas volcano, located in Arequipa, Peru. The proposed classification system achieved a success rate of more than 90%.

リンク情報
DOI
https://doi.org/10.1109/JSTARS.2020.2982714
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