Bayesian ground-roll seperation by curvelet-domain sparsity promotion

TitleBayesian ground-roll seperation by curvelet-domain sparsity promotion
Publication TypeConference
Year of Publication2008
AuthorsCarson Yarham, Felix J. Herrmann
Conference NameSEG Technical Program Expanded Abstracts
Volume27
Page2576-2580
Month11
OrganizationSEG
KeywordsPresentation, SEG, SLIM
Abstract

The removal of coherent noise generated by surface waves in land based seismic is a prerequisite to imaging the subsurface. These surface waves, termed as ground roll, overlay important reflector information in both the t-x and f-k domains. Standard techniques of ground-roll removal commonly alter reflector information. We propose the use of the curvelet domain as a sparsifying transform in which to preform signal-separation techniques that preserves reflector information while increasing ground-roll removal. We look at how this method preforms on synthetic data for which we can build quantitative results and a real field data set.

URLhttps://www.slim.eos.ubc.ca/Publications/Public/Conferences/SEG/2008/yarham08SEGbgr/yarham08SEGbgr.pdf
DOI10.1190/1.3063878
Presentation

https://www.slim.eos.ubc.ca/Publications/Public/Conferences/SEG/2008/yar...

Citation Keyyarham2008SEGbgr