Global ETD Search

1	Multiple prediction from incomplete data with the focused curvelet transform Herrmann, Felix J. 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 curvelet CRSI SRME 2D 3D wavefield reconstruction
2	Surface related multiple prediction from incomplete data Herrmann, Felix J. January 2007 (has links) Incomplete data, unknown source-receiver signatures and free-surface reflectivity represent challenges for a successful prediction and subsequent removal of multiples. In this paper, a new method will be represented that tackles these challenges by combining what we know about wavefield (de-)focussing, by weighted convolutions/correlations, and recently developed curvelet-based recovery by sparsity-promoting inversion (CRSI). With this combination, we are able to leverage recent insights from wave physics towards a nonlinear formulation for the multiple-prediction problem that works for incomplete data and without detailed knowledge on the surface effects. wavefield de-focusing wavefield defocusing weighted convolutions weighted correlations, curvelet based recovery sparsity promoting inversion wavefield focusing curvelet transform
3	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

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