@article{fdi:010079051,
  title = {{A}utomatic multichannel volcano-seismic classification using machine learning and {EMD}},
  author = {{L}ara, {P}. {E}. {E}. and {F}ernandes, {C}. {A}. {R}. and {I}nza, {A}. and {M}ars, {J}. {I}. and {M}{\'e}taxian, {J}ean-{P}hilippe and {D}alla {M}ura, {M}. and {M}alfante, {M}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}his 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 {U}binas volcano, the most active volcano in {P}eru. {T}he 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. {D}ue 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. {T}he presented method is scalable to use data from multiple stations with one or more channels. {T}he 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. {T}he presented investigation was tested with a large database that has a considerable number of explosion events, measured at the {U}binas volcano, located in {A}requipa, {P}eru. {T}he proposed classification system achieved a success rate of more than 90 %.},
  keywords = {{V}olcanoes ; {S}ensors ; {D}atabases ; {C}epstral analysis ; {M}achine learning ; {S}upport vector machines ; {E}xplosions ; {A}rtificial intelligence ; empirical ; mode decomposition ; deconvolution ; time domain analysis ; spectral domain analysis ; cepstral analysis ; seismic signal processing ; {PEROU} ; {UBINAS} {VOLCAN}},
  booktitle = {},
  journal = {{IEEE} {J}ournal of {S}elected {T}opics in {A}pplied {E}arth {O}bservations and {R}emote {S}ensing},
  volume = {13},
  numero = {},
  pages = {1322--1331},
  ISSN = {1939-1404},
  year = {2020},
  DOI = {10.1109/jstars.2020.2982714},
  URL = {https://www.documentation.ird.fr/hor/fdi:010079051},
}