Dissimilarity Measures for the Identification of Earthquake Focal Mechanisms

  • Francesco Benvegna
  • Giosué Lo Bosco
  • Domenico Tegolo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)

Abstract

This work presents a study about dissimilarity measures for seismic signals, and their relation to clustering in the particular problem of the identification of earthquake focal mechanisms, i.e. the physical phenomena which have generated an earthquake. Starting from the assumption that waveform similarity implies similarity in the focal parameters, important details about them can be determined by studying waveforms related to the wave field produced by earthquakes and recorded by a seismic network. Focal mechanisms identification is currently investigated by clustering of seismic events, using mainly cross-correlation dissimilarity in conjunction with hierarchical clustering algorithm. By the way, it results that such adoptions have not been sufficiently validated. To shed light on this we have studied the cross correlation dissimilarity on simulated seismic signals in conjunction with hierarchical and partitional clustering algorithms, and compared its performance with a newly one recently introduced for the purpose called cumulative shape. In particular, we have properly created synthetic waveforms related to two types of focal mechanisms, showing that the cumulative shape perform better than cross-correlation in the identification of the expected clustering solution.

Keywords

metrics clustering seismic signals waveforms 

References

  1. 1.
    Barani, S., Ferretti, G., Massa, M., Spallarossa, D.: The waveform similarity approach to identify dependent events in instrumental seismic catalogues. Geophysical Journal International 168(1), 100–108 (2007)CrossRefGoogle Scholar
  2. 2.
    Badawy, A., Fattah, A.K.A.: Source parameters and fault plane determinations of the 28 december 1999 northeastern cairo earthquakes. Tectonophysics 343, 63–77 (2001)CrossRefGoogle Scholar
  3. 3.
    Benvegna, F., D’Alessando, A., Bosco, G.L., Luzio, D., Pinello, L., Tegolo, D.: A new dissimilarity measure for clustering seismic signals. In: Maino, G., Foresti, G.L. (eds.) ICIAP 2011, Part II. LNCS, vol. 6979, pp. 434–443. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Borman, P.: New Manual of Seismological Observatory Practice. IASPEI, GFZ German Research Centre for Geosciences, Potsdam (2012)Google Scholar
  5. 5.
    Mitchell, W., Aster, R., Young, C., Beiriger, J., Harris, M., Moore, S., Trujillo, J.: A comparison of select trigger algorithms for automated global seismic phase and event detection. Bullettin of the Seismological Society of America 88(1), 95–106 (1998)Google Scholar
  6. 6.
    Stewart, S.W.: Real-time detection and location of local seismic events in central. Bulletin of the Seismological Society of America 67 (1977)Google Scholar
  7. 7.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: a Review. ACM Computing Surveys 31(3), 264–323 (1999)CrossRefGoogle Scholar
  8. 8.
    Giancarlo, R., Bosco, G.L., Pinello, L., Utro, F.: The Three Steps of Clustering in the Post-Genomic Era: A Synopsis. In: Rizzo, R., Lisboa, P.J.G. (eds.) CIBB 2010. LNCS, vol. 6685, pp. 13–30. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Giancarlo, R., Lo Bosco, G., Pinello, L., Utro, F.: A methodology to assess the intrinsic discriminative ability of a distance function and its interplay with clustering algorithms for microarray data analysis. BMC Bioinformatics 14(suppl. 1) (2013)Google Scholar
  10. 10.
    Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley (1990)Google Scholar
  11. 11.
    Shamir, R., Sharan, R.: Algorithmic approaches to clustering gene expression data. In: Current Topics in Computational Biology, pp. 269–300 (2001)Google Scholar
  12. 12.
    Rand, W.M.: Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66(336), 846–850 (1971)CrossRefGoogle Scholar
  13. 13.
    Hubert, L., Arabie, P.: Comparing partitions. Journal of Classification 2, 193–218 (1985)CrossRefGoogle Scholar
  14. 14.
    Madariaga, R.: Dynamics of an expanding circular fault. Bulletin of the Seismological Society of America 66(3), 639–666 (1976)Google Scholar
  15. 15.
    Virieux, J.: P-SV wave propagation in heterogeneous media: Velocity-stress finite-difference method. Geophysics 51(4), 889–901 (1986)CrossRefGoogle Scholar
  16. 16.
    Levander, A.R.: Fourth-order finite-difference p-sv seismograms. Geophysics 53(11), 1425–1436 (1988)CrossRefGoogle Scholar
  17. 17.
    Larsen, S., Harris, D.: Seismic wave propagation through a low-velocity nuclear rubble zone. Technical report, Lawrence Livermore National Lab., CA (1993)Google Scholar
  18. 18.
    Aki, K., Richards, P.G.: Quantitative Seismology: Theory and Methods. Univ. Science Books (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Francesco Benvegna
    • 1
    • 2
  • Giosué Lo Bosco
    • 1
    • 2
    • 3
  • Domenico Tegolo
    • 1
    • 3
  1. 1.Dipartimento di Matematica e InformaticaUniversitá degli Studi di PalermoItaly
  2. 2.I.E.ME.S.T., Istituto Euro Mediterraneo di Scienza e TecnologiaPalermoItaly
  3. 3.C.I.T.C, Centro Interdipartimentale di Teconologie della ConoscenzaPalermoItaly

Personalised recommendations