Global ETD Search

1	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. seismic amplitude recovery curvelets Fourier seismic amplitude recovery normal operatore eigenvalue eigenvector
2	Just diagonalize: a curvelet-based approach to seismic amplitude recovery Herrmann, Felix J., Moghaddam, Peyman P., Stolk, Christiaan C. January 2007 (has links) No description available. curvelet seismic amplitude transform nonlinear approximation wave propagation hessian
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 imaging and processing with curvelets Herrmann, Felix J., Hennenfent, Gilles, Moghaddam, Peyman P. January 2007 (has links) In this paper, we present a nonlinear curvelet-based sparsity-promoting formulation for three problems in seismic processing and imaging namely, seismic data regularization from data with large percentages of traces missing; seismic amplitude recovery for subsalt images obtained by reverse-time migration and primary-multiple separation, given an inaccurate multiple prediction. We argue why these nonlinear formulations are beneficial. subsalt sub-salt seismic data regularization seismic amplitude recovery primary multiple separation wave front detection curvelet invariance sparsity CRSI Migration amplitude recovery: wavefront

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