Global ETD Search

1	Multiple prediction from incomplete data with the focused curvelet transform Herrmann, Felix J., Wang, Deli, Hennenfent, Gilles January 2007 (has links) 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. incomplete data multiple removal CRSI discrete curvelet transform data adaptive transform curvelet regularized inversion data recovery sparse curvelet coefficient vector sparsity propagation focal transform