Disoominkfrcn between gaussian time series based on their spectral differences

Discrimination between two Gaussian time series is examined assuming that the important difference between the alternative processes is their covarianoe (spectral) structure. Using the likelihood ratio method in frequency domain a discriminant function is derived and its approximate distribution is obtained. It is demonstrated that, utilizing the Kullbadk-Leibler information measure, the frequencies or frequency bands which carry information for discrimination can be determined. Using this, it is shown that when mean functions are equal, discrimination based on the frequency with the largest discrimination information is equivalent to the classification procedure based on the best linear discriminant, Application to seismology is described by including a discussion concerning the spectral ratio discriminant for underground nuclear explosion and natural earthquake and is illustrated numerically using Rayleigh wave data from an underground and an atmospheric explosions.