Curvelet-domain multiple elimination with sparseness constraints

TitleCurvelet-domain multiple elimination with sparseness constraints
Publication TypeConference
Year of Publication2004
AuthorsFelix J. Herrmann, Verschuur, DJ
Conference NameSEG Technical Program Expanded Abstracts
Volume23
Page1333-1336
OrganizationSEG
KeywordsPresentation, SEG, SLIM
Abstract

Predictive multiple suppression methods consist of two main steps: a prediction step, in which multiples are predicted from the seismic data, and a subtraction step, in which the predicted multiples are matched with the true multiples in the data. The last step appears crucial in practice: an incorrect adaptive subtraction method will cause multiples to be sub-optimally subtracted or primaries being distorted, or both. Therefore, we propose a new domain for separation of primaries and multiples via the Curvelet transform. This transform maps the data into almost orthogonal localized events with a directional and spatial-temporal component. The multiples are suppressed by thresholding the input data at those Curvelet components where the predicted multiples have large amplitudes. In this way the more traditional filtering of predicted multiples to fit the input data is avoided. An initial field data example shows a considerable improvement in multiple suppression. {\copyright}2004 Society of Exploration Geophysicists

URLhttps://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Herrmann04SEGcdm/Herrmann04SEGcdm.pdf
DOI10.1190/1.1851110
Presentation

https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Herrman...

URL1

http://slim.eos.ubc.ca/ felix/public/SEGM2004.pdf

Citation Keyherrmann2004SEGcdm