In this abstract, we present a nonlinear curvelet-based sparsitypromoting
formulation for the primary-multiple separation
problem. We show that these coherent signal components can
be separated robustly by explicitly exploting the locality of
curvelets in phase space (space-spatial frequency plane) and
their ability to compress data volumes that contain wavefronts.
This work is an extension of earlier results and the presented
algorithms are shown to be stable under noise and moderately
erroneous multiple predictions.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:BVAU.2429/565 |
| Date | January 2007 |
| Creators | Wang, Deli, Saab, Rayan, Yilmaz, Ozgur, Herrmann, Felix J. |
| Publisher | Society of Exploration Geophysicists |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | text |
| Rights | Herrmann, Felix J. |
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