Autoregressive Representation of Seismic P-wave Signals with an Application to the Problem of Short-Period Discriminants

TLDR: In this article, it was shown that seismic P-wave signals can be represented by parametric models of autoregressive type, which are models having the form X(t)-a1X(t-p)-1)- apX( t-p) =Z(t) where X(p) is the digitized short-period data time series defined by the P-Wave signal, and Z(t), is a white noise series.

Abstract: Summary It is shown that seismic P-wave signals can be represented by parametric models of autoregressive type. These are models having the form X(t)-a1X(t–1)-…- apX(t-p) =Z(t) where X(t) is the digitized short-period data time series defined by the P-wave signal, and Z(t) is a white noise series. The autoregressive analysis is undertaken for 40 underground nuclear explosions and 45 earthquakes from Eurasia. For each event a separate analysis of the noise preceding the event as well as of the P-wave coda has been included. It is found that in most cases a reasonable statistical fit is obtained using a low order autoregressive model. The autoregressive parameters characterize the power spectrum (equivalently, the autocorrelation function) of the P-wave signal and form a convenient basis for studying the possibilities of short-period discrimination between nuclear explosions and earthquakes. A preliminary discussion of these possibilities is included.