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3D targeted multiple attenuation[Note 1. Received June 1996, revision accepted November 1996. ...]
Author(s) -
Kneib G.,
Bardan V.
Publication year - 1997
Publication title -
geophysical prospecting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 79
eISSN - 1365-2478
pISSN - 0016-8025
DOI - 10.1046/j.1365-2478.1997.460276.x
Subject(s) - multiple , algorithm , deconvolution , attenuation , computation , amplitude , flattening , geology , covariance matrix , aliasing , computer science , offset (computer science) , covariance , mathematics , filter (signal processing) , physics , optics , statistics , computer vision , arithmetic , astronomy , programming language
Short‐period multiple reflections pose a particular problem in the North Sea where predictive deconvolution is often only partially successful. The targeted multiple attenuation (TMA) algorithm comprises computation of the covariance matrix of preflattened prestack or post‐stack seismic data, the determination of the dominating eigenvectors of the covariance matrix, and subtraction of the related eigenimages followed by reverse flattening. The main assumption made is that the flattened multiple reflections may be represented by the first eigenimage(s) which implies that the spatial amplitude variations of primaries and associated multiples are similar. This assumption usually limits the method to short‐period multiple reflections. TMA is applicable post‐stack or prestack to common‐offset gathers. It is computationally fast, robust towards random noise, irregular geometry and spatial aliasing, and it preserves the amplitudes of primaries provided they are not parallel to the targeted multiples. Application of TMA to 3D wavefields is preferable because this allows a better discrimination between primaries and multiples. Real data examples show that the danger of partially removing primary energy can be reduced by improving the raw multiple model that is based on eigenimages, for example by prediction filtering.