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Feature Extraction Using Circular Statistics Applied to Volcano Monitoring

  • César San-Martin
  • Carlos Melgarejo
  • Claudio Gallegos
  • Gustavo Soto
  • Millaray Curilem
  • Gustavo Fuentealba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6419)

Abstract

In this work, the applicability of the circular statistics to feature extraction on seismic signals is presented. The seismic signals are captured from Llaima Volcano, located in Southern Andes Volcanic Zone at 38°40’S 71°40’W. Typically, the seismic signals can be divided in long-period, tremor, and volcano-tectonic earthquakes. The seismic signals are time-segmented using a rectangular window of 1 minute of duration. In each segment, the instantaneous phase is calculated using the Hilbert Transform, and then, one feature is obtained. Thus, the principal hypothesis of this work is that the instantaneous phase can be assumed as a circular random variable in [0,2 π) interval. A second feature is obtained using the wavelet transform due to the fact that seismic signals present high energy located in low frequency. Then, in the range 1.55 and 3.11 Hz the wavelet coefficients were obtained and their mean energy is calculated as the second feature. Real seismic data represented using this two features are classified using a linear discriminant with a 92.5% of correct recognition rate.

Keywords

seismic classifications feature extraction circular statistic wavelet transform 

References

  1. 1.
    Shick, R.: Volcanic Tremor- Source Mechanism And Correlation With Eruptive Activity. Natural Hazard, 125–144 (1988)Google Scholar
  2. 2.
    Minakami, T.: Seismology of Volcanoes in Japan. Physical Volcanology. Developments In Solid Earth Geophysics 6 (1982)Google Scholar
  3. 3.
    Chouet, B.: Volcano Seismology As An Approach To Eruption Forecasting. Submitted To Nature, Draft Of (1993)Google Scholar
  4. 4.
    Orlic, N., Loncaric, S.: Earthquake-explosion discrimination using genetic algorithm-based boosting approach. Computers and Geosciences 36(2), 179–185 (2010)CrossRefGoogle Scholar
  5. 5.
    Scarpetta, S., Giudicepietro, F., Ezin, E.C., Petrosino, S., Del Pezzo, E., Martín, M., Marinaro, M.: Automatic Classification of Seismic Signals at Mt Vesuvius Volcano, Italy, using Neural Networks. Bulletin of the Seismological Society of America 95(1), 185–196 (2005)CrossRefGoogle Scholar
  6. 6.
    Langer, H., Falsaperla, S., Powell, T., Thompson, G.: Automatic classification and a-posteriori analysis of seismic event identification at Soufrière Hills volcano, Monserrat. Journal of Volcanology and Geothermal Research 153, 1–10 (2006)CrossRefGoogle Scholar
  7. 7.
    Curilem, G.M.S., Vergara, J., Fuentealba, G., Acuña, G., Chacón, M.: Classification of Seismic Signals at Villarrica Volcano (Chile) using Neural Networks and Genetic Algorithms. Journal of Volcanology and Geothermal Research 180(1), 1–8 (2009)CrossRefGoogle Scholar
  8. 8.
    Gendron, P., Nandram, B.: An empirical Bayes estimator of seismic events using wavelet packet bases. Journal of Agricultural, Biological, and Environmental Statistics 6(3), 379–402 (2001)CrossRefGoogle Scholar
  9. 9.
    Erlebacher, G., Yuen, D.A.: A wavelet toolkit for visualization and analysis of large data sets. Earthquake Research, Pure Appl. Geophys. 161, 2215–2229 (2004)Google Scholar
  10. 10.
    Lesage, P., Glangeaud, F., Mars, J.: Applications of autoregressive models and time-frequency analysis to the study of volcanic tremor and long-period events. Journal of Volcanology and Geothermal Research 114, 391–417 (2002)CrossRefGoogle Scholar
  11. 11.
    Ibanez, J.M., Benitez, C., Gutierrez, L.A., Cortés, G., García-Yeguas, A., Alguacil, G.: The classification of seismo-volcanic signals using Hidden Markov Models as applied to the Stromboli and Etna volcanoes. Journal of Volcanology and Geothermal 187(3-4), 218–226 (2009)CrossRefGoogle Scholar
  12. 12.
    Dowla, F.U.: Neural networks in seismic discrimination. In: Husebye, E.S., Dainty, A.M. (eds.) NATO ASI (Advanced Science Institutes). Series E, vol. 303, pp. 777–789. Kluwer, Dordrecht (1995)Google Scholar
  13. 13.
    Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Inc., New York (1995)zbMATHGoogle Scholar
  14. 14.
    Luongo, G., Marandola, C., Mazzarella, A.: Neural forecasting of seismicity and ground displacements in different volcanic areas. Journal of Volcanology and Geothermal Research 130, 133–146 (2004)CrossRefGoogle Scholar
  15. 15.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefzbMATHGoogle Scholar
  16. 16.
    Cherkassky, V., Krasnopolsky, V., Solomatine, D.P., Valdes, J.: Computational intelligence in earth sciences and environmental applications: Issues and challenges. Neural Networks 19(2), 113–121 (2006)CrossRefzbMATHGoogle Scholar
  17. 17.
    Langer, H., Falsaperla, S., Masotti, M., Campanini, R., Spampinato, S., Messina, A.: Synopsis of supervised and unsupervised pattern classification techniques applied to volcanic tremor data at Mt Etna. Italy Geophysical Journal International 178(2), 1132–1144 (2009)CrossRefGoogle Scholar
  18. 18.
    Giacco, F., Esposito, A.M., Scarpetta, S., Giudicepietro, F., Marinaro, M.: Support Vector Machines and MLP for automatic classification of seismic signals at Stromboli volcano. In: Proceeding of the 2009 conference on Neural Nets WIRN 2009: Proceedings of the 19th Italian Workshop on Neural Nets. Frontiers in Artificial Intelligence and Applications, vol. 204, pp. 116–123 (2009)Google Scholar
  19. 19.
    Nunes, J.-C., Naït-Ali, A.: Hilbert transform-based ECG modeling. Biomedical Engineering 39, 133–137 (2005)CrossRefGoogle Scholar
  20. 20.
    Cohen, L.: Time-frequency analysis. Prentice-Hall, Englewood Cliffs (1995)Google Scholar
  21. 21.
    Fisher, N.I.: Statistical Analysis of Circular Data. Cambridge University Press, Cambridge (1995)Google Scholar
  22. 22.
    Jammalamadaka, S.R., SenGupta, A.: Topics in circular statistics. World Scientific Publishing, Singapore (2001)CrossRefGoogle Scholar
  23. 23.
    Mallat, S.: A wavelet tour of signal processing, 2nd edn. Academic Press, London (1999)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • César San-Martin
    • 1
    • 4
  • Carlos Melgarejo
    • 1
    • 2
  • Claudio Gallegos
    • 1
    • 2
  • Gustavo Soto
    • 3
  • Millaray Curilem
    • 4
  • Gustavo Fuentealba
    • 5
  1. 1.Information Processing Laboratory, Department of Electrical EngineeringUniversidad de La FronteraCasillaChile
  2. 2.Observatorio Volcanológico de los Andes del Sur Dinamarca 691, TemucoChile
  3. 3.Center for Mathematical ModelUniversidad de ChileCasillaChile
  4. 4.Department of Electrical EngineeringUniversidad de La FronteraCasillaChile
  5. 5.Department of PhysicsUniversidad de La FronteraCasillaChile

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