It is shown that seismic P-wave vector signals as recorded by selected NORSAR subarrays can be described by multivariate parametric models of autoregressive type. These are models having the form
Where (t) is the digitized short-period vector time series defined by the P-wave signal and (t) is a white noise vector time series. The multivariate autoregressive analysis is undertaken for 83 nuclear explosions and 72 earthquakes from Eurasia. For each event a separate analysis of the main signal and of the coda has been carried through. It is found that in most cases a reasonable statistical fit is obtained using a low-order autoregressive model. The autoregressive parameters characterize the spectral density matrix of the P-wave signal and therefore form a convenient basis for constructing short-period discriminants between earthquakes and explosions. Based on the classification results for our data base of Eurasian events, we find that the multivariate autoregressive parameters have a substantially larger discrimination potential than the short-period parameters suggested earlier in the literature. In fact our results indicate that, based on autoregressive parameters, it may now be possible to construct purely short-period discriminants which are comparable, if not superior, to the mb:Ms criterion.