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Bayesian wavefield separation by transform-domain sparsity promotion

Deli Wang% latex2html id marker 2929
\setcounter{footnote}{1}\fnsymbol{footnote}, Rayan Saab% latex2html id marker 2930
\setcounter{footnote}{2}\fnsymbol{footnote}, Özgür Yilmaz% latex2html id marker 2931
\setcounter{footnote}{3}\fnsymbol{footnote}and Felix J. Herrmann% latex2html id marker 2932
\setcounter{footnote}{4}\fnsymbol{footnote}


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.




next up previous [pdf]

Next: Introduction Up: Reproducible Documents

2008-03-13