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A New Dissimilarity Measure for Clustering Seismic Signals

  • Francesco Benvegna
  • Antonino D’Alessando
  • Giosuè Lo Bosco
  • Dario Luzio
  • Luca Pinello
  • Domenico Tegolo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6979)

Abstract

Hypocenter and focal mechanism of an earthquake can be determined by the analysis of signals, named waveforms, related to the wave field produced and recorded by a seismic network. Assuming that waveform similarity implies the similarity of focal parameters, the analysis of those signals characterized by very similar shapes can be used to give important details about the physical phenomena which have generated an earthquake. Recent works have shown the effectiveness of cross-correlation and/or cross-spectral dissimilarities to identify clusters of seismic events. In this work we propose a new dissimilarity measure between seismic signals whose reliability has been tested on real seismic data by computing external and internal validation indices on the obtained clustering. Results show its superior quality in terms of cluster homogeneity and computational time with respect to the largely adopted cross correlation dissimilarity.

Keywords

Body Wave Seismic Signal Dissimilarity Measure Cumulative Energy Ocean Bottom Seismometer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Francesco Benvegna
    • 1
  • Antonino D’Alessando
    • 3
  • Giosuè Lo Bosco
    • 1
  • Dario Luzio
    • 2
  • Luca Pinello
    • 1
  • Domenico Tegolo
    • 1
  1. 1.Dipartimento di Matematica e InformaticaPalermoItaly
  2. 2.Dipartimento di Fisica e Chimica della terraPalermoItaly
  3. 3.Centro Nazionale TerremotiIstituto Nazionale di Geofisica e VulcanologiaItaly

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