A number of earthquake/explosion discriminants outlined in Pomeroy et al. (1982) are evaluated in the western United States. The data consist of 233 NTS explosions and 130 western U.S. earthquakes in the magnitude range of 2.5 to 6.5 recorded at four broadband seismic stations operated by Lawrence Livermore National Laboratory (LLNL). The stations surround NTS at distances of about 200 to 400 km. The propagation paths for the earthquakes range from approximately 175 to 1300 km and are confined mainly to the Basin and Range. The discriminants tested include mb − Ms, mb, − Msh (higher mode magnitude), long-period Love and Rayleigh wave energy density and their ratio, relative excitation of short-period SH waves, Lg/Pg, Lg/Rg, Lg/Sm short-period amplitude ratios, spectral ratios in PN, Pg and Lg, and third moment of frequency.
In general, the long-period discriminants exhibit the best separation between the earthquake and explosion populations. However, the long-period measurements are often difficult to make, especially at small magnitudes. Thus, it is necessary to supplement the long-period discriminants with short-period time- and frequency-domain discriminants. The short-period time-domain discriminants generally were characterized by very poor discriminant performance. However, because of the good signal to noise ratio for many of the regional phases (such as Lg and Pg), a large number of measurements could be acquired down to small magnitudes. The usefulness of the short-period time-domain discriminants lies in the fact that when an event shows a ratio above a certain threshold, it can generally be correctly identified. Spectral discriminants appear to have good potential, even at relatively small magnitudes. The measurements are simple to make, and first-order distance corrections can easily be applied. However, a number of uncertainties regarding the lack of a physical understanding of how the spectral discriminants work makes their utilization questionable at this point.
A multivariate discrimination technique is described that is designed to handle missing data while providing optimum classification of an unknown event based on a given set of discrimination variables. The discrimination variables are first transformed to be insensitive to magnitude, path, or features of the test program. Tests show that the commonly used assumption of equal covariance matrices for the earthquake and explosion populations (resulting in a linear discrimination function) is not valid. However, a quadratic discrimination function appears to be appropriate. An optimality criterion, misclassification cost minimization, is used to select the best set of discrimination variables at each station. Application of the multivariate technique to individual stations for the entire dataset shows misclassification rates ranging from 3 to 10 per cent and 4 to 26 per cent for explosions and earthquakes, respectively. Multistation discrimination shows misclassification probabilities of 0 to 2 per cent for explosions and 3 to 4 percent for earthquakes.
Most misclassifications occur for mb < 4.0 where it is often difficult to obtain long-period measurements that perform well. Earthquakes show higher misclassification rates than explosions presumably because of their wider range of paths, depth, and focal mechanisms. Significant station variability in discrimination performance is observed, but we are unable to separate out effects of receiver structure from path effects.