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 X¯(t) (t) is the digitized short-period vector time series defined by the P-wave signal and W¯(t) (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.

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