Application to Two Mutivariate Classification Techniques to the Problem of Seismic Discrimination

摘要

Two multivariate classification methods have been applied to discriminant variables measured on a set of 20 Nevada Test Site (NTS) underground nuclear explosions and 27 North American Earthquakes. The classification methods used in this study are cluster analysis, and linear and quadratic multiple discriminant analysis. Discriminant analysis requires that the parameters of the underlying stochastic model be specified a priori, which is usually accomplished with a sample training set of events. Cluster analysis, however, follows a non-parametric approach and requires no prior information. The discriminant variables are the seven energy ratios calculated for these events by Booker and Mitronovas (1964). An additional discriminant variable, M(s)/m(b) was estimated for a reduced set of nine earthquakes and 21 underground explosions. Four M(s)/m(b) estimates were obtained for each event in this subset by randomly generating M(s) estimates within four specified variance levels about the M(s) versus M(b) lines given by Alexander and Lambert (1973). The variances used in generating the M(s) estimates are multiples of the variance found by von Seggern (1972) to be representative of M(s) measurements for NTS explosions. (Author)