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Bayesian wavefield separation by transform-domain sparsity
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Deli Wang, Rayan Saab,
Özgür Yilmazand Felix
J. Herrmann
Abstract:
Successful removal of coherent noise sources greatly determines the
quality of seismic imaging. Major advances were made in this
direction, e.g., Surface-Related Multiple
Elimination (SRME) and interferometric ground-roll removal. Still,
moderate phase, timing, amplitude errors and clutter in the
predicted signal components can be detrimental. Adopting a Bayesian
approach along with the assumption of approximate curvelet-domain
independence of the to-be-separated signal components, we construct
an iterative algorithm that takes the predictions produced by for
example SRME as input and separates these components in a robust
fashion. In addition, the proposed algorithm controls the energy
mismatch between the separated and predicted components. Such a
control, which was lacking in earlier curvelet-domain formulations,
produces improved results for primary-multiple separation on both
synthetic and real data.
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| Bayesian wavefield separation by transform-domain sparsity
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2008-03-13