An investigation is made of the application of the autocorrelation matrix method to the analysis of moveout. A number of multichannel Wiener filters are computed to pass/reject correlated events. Experiments are performed on a variety of synthetic data in order to ascertain how such filters will perform in a given situation. It is shown that very low levels of signal can be successfully recovered and a very close match must exist between the moveouts present in the data and those specified for the particular filter design. The 'average' or second order autocorrelation matrix is developed, and a multichannel filter scan of a column from this matrix is incorporated in the former method. Experiments are performed on synthetic data for different noise conditions in order to ascertain the ability of these methods to detect low-power correlated events. It is shown that such detection can be successful, provided the random noise is not too severe. Results from the analyses of real data records are consistent with those of the synthetic case.

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