Improving the Classification of Volcanic Seismic Events Extracting New Seismic and Speech Features

  • Millaray Curilem
  • Camilo Soto
  • Fernando Huenupan
  • Cesar San Martin
  • Gustavo Fuentealba
  • Carlos Cardona
  • Luis Franco
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)

Abstract

This paper presents a study on features extracted from the seismic and speech domains that were used to classify four groups of seismic events of the Llaima volcano, located in the Araucanía Region of Chile. 63 features were extracted from 769 events that were labeled and segmented by experts. A feature selection process based on a genetic algorithm was implemented to select the best descriptors for the classifying structure formed by one SVM for each class. The process identified a few features for each class, and a performance that overcame the results of previous similar works, reaching over that 95% of exactitude and showing the importance of the feature selection process to improve classification. These are the newest results obtained from a technology transfer project in which advanced signal processing tools are being applied, in collaboration with the Southern Andes Volcano Observatory (OVDAS), to develop a support system for the monitoring of the Llaima volcano.

Keywords

Volcanic seismicity Pattern recognition SVM Feature selection 

Notes

Acknowledgements

We would like to thank FONDEF as this study is being supported by the project FONDEF IDeA IT15I10027.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Millaray Curilem
    • 1
  • Camilo Soto
    • 1
  • Fernando Huenupan
    • 1
  • Cesar San Martin
    • 1
  • Gustavo Fuentealba
    • 1
  • Carlos Cardona
    • 2
  • Luis Franco
    • 2
  1. 1.Facultad de Ingeniería y CienciasUniversidad de La FronteraTemucoChile
  2. 2.Observatorio Vulcanológico de los Andes SurTemucoChile

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