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Curvelet-domain least-squares migration with sparseness constraints.

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Title: Curvelet-domain least-squares migration with sparseness constraints.
Author: Herrmann, Felix J.; Moghaddam, Peyman P.
Subject Keywords curvelet;least squares migration;sparseness
Issue Date: 2004
Publicly Available in cIRcle 2008-02-25
Publisher European Association of Geoscientists and Engineers
Citation: Herrmann, Felix J., Moghaddam, Peyman. Curvelet-domain least-squares migration with sparseness constraints. 2004. EAGE 66th Conference & Exhibition Proceedings.
Abstract: A non-linear edge-preserving solution to the least-squares migration problem with sparseness constraints is introduced. The applied formalism explores Curvelets as basis functions that, by virtue of their sparseness and locality, not only allow for a reduction of the dimensionality of the imaging problem but which also naturally lead to a non-linear solution with significantly improved signalto-noise ratio. Additional conditions on the image are imposed by solving a constrained optimization problem on the estimated Curvelet coefficients initialized by thresholding. This optimization is designed to also restore the amplitudes by (approximately) inverting the normal operator, which is like-wise the (de)-migration operators, almost diagonalized by the Curvelet transform.
Affiliation: Science, Faculty ofEarth and Ocean Sciences, Department of
URI: http://hdl.handle.net/2429/452
Peer Review Status: Unreviewed
Scholarly Level: Faculty

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