A note on autoregressive spectral estimates for frequency-wavenumber analysis of strong-motion array data

Frequency-wavenumber (f-k) spectra of seismic strong-motion array data are useful in estimating back-azimuth and apparent propagation velocity of seismic waves arriving at the array. Such estimates are required to model wave passage effects while studying spatial variability of strong ground motion. Although periodogram-based spectral estimates are commonly used, practical applications based on them encounter limitations, such as, lack of objective criteria for selecting a proper smoothing window and its associated bandwidth, and relatively large variance of the estimated spectral quantities. We present an alternative spectral estimate based on parametric time series modelling approach. The well-known autoregressive (AR) time series model is used in a system-based approach to estimate the spectral matrix of auto- and cross-spectral densities. Such spectral estimates are found to be smoother than the windowed periodogram estimates, and can directly be used in f-k spectral analysis. We present an example application of the proposed technique using strong-motion data recorded by the SMART-1 array in Taiwan during the January 29 1981 6.3 earthquake. Our results, in terms of back azimuth and apparent propagation velocity, are found to be in excellent agreement with those reported in the literature.