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Multiple prediction from incomplete data with the focused curvelet transform

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Title: Multiple prediction from incomplete data with the focused curvelet transform
Author: Herrmann, Felix J.; Wang, Deli; Hennenfent, Gilles
Subject Keywords incomplete data;multiple removal;curvelet based recovery by sparsity-promoting inversion;CRSI;discrete curvelet transform;data adaptive transform;curvelet regularized inversion;data recovery;sparse curvelet coefficient vector;sparsity;propagation;focal transform
Issue Date: 2007
Publicly Available in cIRcle 2008-03-11
Publisher Society of Exploration Geophysicists
Citation: Herrmann, Felix J., Wang, Deli, Hennenfent, Gilles. 2007. Multiple prediction from incomplete data with the focused curvelet transform. SEG International Exposition and 77th Annual Meeting.
Abstract: Incomplete data represents a major challenge for a successful prediction and subsequent removal of multiples. In this paper, a new method will be represented that tackles this challenge in a two-step approach. During the first step, the recenly developed curvelet-based recovery by sparsity-promoting inversion (CRSI) is applied to the data, followed by a prediction of the primaries. During the second high-resolution step, the estimated primaries are used to improve the frequency content of the recovered data by combining the focal transform, defined in terms of the estimated primaries, with the curvelet transform. This focused curvelet transform leads to an improved recovery, which can subsequently be used as input for a second stage of multiple prediction and primary-multiple separation.
Affiliation: Earth and Ocean Sciences, Dept. of (EOS), Dept of
URI: http://hdl.handle.net/2429/563
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