Recent results in curvelet-based primary-multiple separation: application to real data
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 pla...
| Main Authors: | , , , |
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| Language: | English |
| Published: |
Society of Exploration Geophysicists
2008
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| Subjects: | |
| Online Access: | http://hdl.handle.net/2429/565 |
| Summary: | 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. |
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