Teleseismic Discrimination of Earthquakes and Nuclear detonations with Features Derived from Maximum Entropy Power spectral Estimates

This paper describes pattern recognition techniques for discriminating between earthquakes and underground nuclear detonations from teleseismic signals. The features investigated are obtained by Maximum Entropy Spectral Analysis techniques. Classification performance is evaluated on a seismic database recorded at the Large Aperture Seismic Array (LASA) in Montana. The events are randomly divided into design and test subsets. Several pattern recognition algorithms including Baye's quadratic classifier, the minimum distance classifier and the nearest neighbor rule are applied. The best results are obtained with spectral ratios from windows taken at regular intervals within the coda combined with bodywave magnitude. With the Baye's classifier, 85% correct classification was achieved for the test set events. For a reduced dataset containing only central Asian events, 96% was achieved.