Spectral Characterization of Volcanic Earthquakes at Nevado del Ruiz Volcano Using Spectral Band Selection/Extraction Techniques

  • Mauricio Orozco-Alzate
  • Marina Skurichina
  • Robert P. W. Duin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)


Spectral content of seismic signals contains essential information for discriminating different classes of volcanic earthquakes. Such an information is largely redundant; therefore, a reduce number of spectral regions may provide almost the same description of the original events. By reducing the number of bands considered, the amount of data to be processed is significantly decreased and the interpretability of the characterization results is enhanced as well. We consider several spectral band selection methods in a two-class classification problem of volcanic earthquakes recorded at Nevado del Ruiz Volcano. Selection approaches have been compared to each other in terms of classification accuracy as well as by looking at the resulting spectral divisions. Detailed discussions about the technical considerations of the selection approaches as well as regarding their possible physical interpretations have been conducted. Results show that the sequential selection approach is the most flexible and powerful for classifying and characterizing volcanic earthquakes.


Signal processing and analysis statistical pattern recognition seismic-volcanic events spectral analysis 


  1. 1.
    Zamora-Camacho, A., Espíndola, J.M., Reyes-Dávila, G.: The 1997–1998 activity of volcán de Colima, Western Mexico: Some aspects of the associated seismic activity. Pure Appl. Geophys. 164, 39–52 (2007)CrossRefGoogle Scholar
  2. 2.
    Chouet, B.A.: Longperiod volcano seismicity: its source and use in eruption forecasting. Nature 380, 309–316 (1996)CrossRefGoogle Scholar
  3. 3.
    Zobin, V.: Introduction to Volcanic Seismology. Elsevier, Amsterdam (2003)Google Scholar
  4. 4.
    Jousset, P., Neuberg, J., Jolly, A.: Modelling low-frequency volcanic earthquakes in a viscoelastic medium with topography. Geophys. J. Int. 159, 776–802 (2004)CrossRefGoogle Scholar
  5. 5.
    Skurichina, M., Verzakov, S., Paclík, P., Duin, R.P.W.: Effectiveness of spectral band selection/extraction techniques for spectral data. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 541–550. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Orozco-Alzate, M., García-Ocampo, M.E., Duin, R.P.W., Castellanos-Domínguez, C.G.: Dissimilaritybased classification of seismic volcanic signals at Nevado del Ruiz volcano. ESRJ 10, 57–65 (2006)Google Scholar
  7. 7.
    Kumar, S., Ghosh, J., Crawford, M.M.: Bestbases feature extraction algorithms for classification of hyperspectral data. IEEE Trans. Geosci. Remote Sensing 39, 1368–1379 (2001)CrossRefGoogle Scholar
  8. 8.
    Skurichina, M., Paclík, P., Duin, R.P.W., de Veld, D., Sterenborg, H.J.C.M., Witjes, M.J.H., Roodenburg, J.L.N.: Selection/extraction of spectral regions for autofluorescence spectra measured in the oral cavity. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds.) SSPR&SPR 2004. LNCS, vol. 3138, pp. 1096–1104. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Meloni, S.: Finding discriminative bands in autofluorescence spectra for automatic cancer diagnosis. Master’s thesis, Cagliari University, Sardinia, Italy (2004)Google Scholar
  10. 10.
    Friedman, J.H.: Regularized discriminant analysis. J. Amer. Statist. Assoc. 84, 165–175 (1989)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Mauricio Orozco-Alzate
    • 1
  • Marina Skurichina
    • 2
  • Robert P. W. Duin
    • 2
  1. 1.Departamento de Informática y ComputaciónUniversidad Nacional de Colombia Sede ManizalesManizales (Caldas)Colombia
  2. 2.Information and Communication Theory GroupDelft University of TechnologyDelftThe Netherlands

Personalised recommendations