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Two methods for subtracting surface-related multiples were presented.
One method removes the multiples by adaptive subtraction, assuming
that the primaries have minimum energy. One method removes the
multiples with a pattern-recognition technique, assuming that the
primaries and multiples have different multivariate spectra (patterns).
Tests on a 2D synthetic dataset show that the pattern-based technique
tends to separate primaries and multiples better than adaptive
subtraction. In cases where the primaries and multiples are
correlated, however, pattern-recognition can damage primaries. This
effect is amplified by the Spitz approximation which prevents the
noise and signal PEFs from spanning similar areas of the data
space.

** Next:** Acknowledgments
** Up:** Guitton: Adaptive subtraction vs.
** Previous:** Signal/noise separation results
Stanford Exploration Project

5/23/2004