Parametric and Nonparametric Discriminants for Regional Earthquakes and Explosions
CALIFORNIA UNIV DAVIS INTERCOLLEGE DIV OF STATISTICS
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Conventional methods for discriminating between earthquakes and explosions at regional distances have concentrated on extracting specific features such as amplitude and spectral ratios from the waveforms of the P and S phases. We consider here an optimum nonparametric classification procedure derived from the classical approach to discriminating between two Gaussian processes with unequal spectra. Two robust variations based on the minimum discrimination information statistic and Renyis entropy are also considered. We compare the optimum classification procedure with various amplitude and spectral ratio discriminants and show that its performance is superior when applied to a small population of 8 land-based earthquakes and 8 mining explosions recorded in Scandinavia. Several parametric characterizations of the notion of complexity based on modeling earthquakes and explosions as autoregressive or modulated autoregressive processes are also proposed and their performance compared with the nonparametric and feature extraction approaches.
- Nuclear Weapons