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Multidimensional Discrimination Techniques—Theory and Application

  • Dag Tjøstheim
Part of the NATO Advanced Study Institutes Series book series (ASIC, volume 74)

Abstract

The problem of discriminating between earthquakes and underground nuclear explosions is formulated as a problem in pattern recognition. As such it may be separated into two stages, feature extraction and classification. Various ways of doing feature extraction will be discussed. Among the techniques mentioned will be univariate and multivariate autoregressive representation, Karhunen-Loève expansions, geophysical parameters and spectral parameters. The ordinary multivariate Gaussian classification algorithm will be reviewed, but some more recent methods will also be mentioned and practical problems in designing a classifier will be discussed. The theory described will be illustrated on a relatively large data base of Eurasian earthquakes and explosions.

Keywords

Feature Vector Feature Extraction Rayleigh Wave Principal Component Method Underground Nuclear Explosion 
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

© D. Reidel Publishing Company 1981

Authors and Affiliations

  • Dag Tjøstheim
    • 1
  1. 1.Norwegian School of Economics and Business AdministrationBergenNorway

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