@article{fdi:010074919,
  title = {{A}utomatic classification of volcano seismic signatures},
  author = {{M}alfante, {M}. and {D}alla {M}ura, {M}. and {M}ars, {J}. {I}. and {M}{\'e}taxian, {J}ean-{P}hilippe and {M}acedo, {O}. and {I}nza, {A}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he prediction of volcanic eruptions and the evaluation of associated risks remain a timely and unresolved issue. {T}his paper presents a method to automatically classify seismic events linked to volcanic activity. {A}s increased seismic activity is an indicator of volcanic unrest, automatic classification of volcano seismic events is of major interest for volcano monitoring. {T}he proposed architecture is based on supervised classification, whereby a prediction model is built from an extensive data set of labeled observations. {R}elevant events should then be detected. {T}hree 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. {O}ur main contribution lies in the feature space where the seismic observations are represented by 102 features gathered from both acoustic and seismic fields. {I}deally, observations are separable in the feature space, depending on their class. {T}he architecture is tested on 109,609 seismic events that were recorded between {J}une 2006 and {S}eptember 2011 at {U}binas {V}olcano, {P}eru. {S}ix main classes of signals are considered: long-period events, volcanic tremors, volcano tectonic events, explosions, hybrid events, and tornillos. {O}ur model reaches 93.5%0.50% accuracy, thereby validating the presented architecture and the features used. {F}urthermore, 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. {T}he model is then used to analyze 6years of data.},
  keywords = {volcano seismic signal ; automatic classification ; machine learning ; {U}binas {V}olcano ; volcano monitoring ; volcanic hazards ; {PEROU} ; {UBINAS} {VOLCAN}},
  booktitle = {},
  journal = {{J}ournal of {G}eophysical {R}esearch : {S}olid {E}arth},
  volume = {123},
  numero = {12},
  pages = {10645--10658},
  ISSN = {2169-9313},
  year = {2018},
  DOI = {10.1029/2018jb015470},
  URL = {https://www.documentation.ird.fr/hor/fdi:010074919},
}