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Primaries (signal) and multiples (noise) exhibit often different kinematics and
amplitudes (i.e., patterns) in time and space. Multidimensional
prediction-error filters (PEFs) approximate these
patterns to separate noise and signal in a least-square sense. These
filters are time-space variant to handle the non-stationarity of
multi-offset seismic data. PEFs for the primaries and multiples are
estimated from pattern models. In an ideal case where accurate
pattern models of both noise and signal exist, the pattern-based method
recovers the primaries while preserving their amplitudes. In the more
general case, the pattern model of the multiples is obtained by using
the data as prediction operators. The pattern model of the primaries
is obtained by convolving the noise PEFs with the input data.
In this situation, 3-D PEFs are preferred to separate (in prestack
data) the multiples properly and to preserve the primaries.
Comparisons of the proposed method with adaptive
subtraction with a norm demonstrate that for a given multiple model, the
pattern-based approach attenuates the multiples and recovers the
primaries generally better. In addition, tests on a 2-D line from the
Gulf of Mexico demonstrate that the proposed technique copes fairly
well with modeling inadequacies present in the multiple prediction.

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Stanford Exploration Project

5/5/2005