Global ETD Search

1	Robust curvelet-domain data continuation with sparseness constraints. Herrmann, Felix J. January 2005 (has links) A robust data interpolation method using curvelets frames is presented. The advantage of this method is that curvelets arguably provide an optimal sparse representation for solutions of wave equations with smooth coefficients. As such curvelets frames circumvent - besides the assumption of caustic-free data - the necessity to make parametric assumptions (e.g. through linear/parabolic Radon or demigration) regarding the shape of events in seismic data. A brief sketch of the theory is provided as well as a number of examples on synthetic and real data. curvelets frames sparseness linear radon demigration parabolic radon
2	Seismic imaging and processing with curvelets Herrmann, Felix J. January 2007 (has links) No description available. curvelet transform nonlinear sparsity data recovery seismic demigration migration wavefront detection continuity scaling
3	Robust seismic amplitude recovery using curvelets Moghaddam, Peyman P., Herrmann, Felix J., Stolk, Christiaan C. January 2007 (has links) In this paper, we recover the amplitude of a seismic image by approximating the normal (demigrationmigration) operator. In this approximation, we make use of the property that curvelets remain invariant under the action of the normal operator. We propose a seismic amplitude recovery method that employs an eigenvalue like decomposition for the normal operator using curvelets as eigen-vectors. Subsequently, we propose an approximate non-linear singularity-preserving solution to the least-squares seismic imaging problem with sparseness in the curvelet domain and spatial continuity constraints. Our method is tested with a reverse-time ’wave-equation’ migration code simulating the acoustic wave equation on the SEG-AA salt model. demigration migration operator curvelets amplitude recovery invariance seismic amplitude amplitude correction signal recovery diagonal approximation
4	Seismic data processing with curvelets: a multiscale and nonlinear approach Herrmann, Felix J. January 2007 (has links) In this abstract, we present a nonlinear curvelet-based sparsity-promoting formulation of a seismic processing flow, consisting of the following steps: seismic data regularization and the restoration of migration amplitudes. We show that the curvelet's wavefront detection capability and invariance under the migration-demigration operator lead to a formulation that is stable under noise and missing data. migration demigration curvelet seismic data regularization restoration migration amplitudes nonlinear curvelet domain sparsity smoothness overfitting 2D 2-D

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