Multiple prediction from incomplete data with the focused curvelet transform

TitleMultiple prediction from incomplete data with the focused curvelet transform
Publication TypeConference
Year of Publication2007
AuthorsFelix J. Herrmann
Conference NameSEG Technical Program Expanded Abstracts
KeywordsPresentation, SEG, SLIM
Abstract

Incomplete data represents a major challenge for a successful prediction and subsequent removal of multiples. In this paper, a new method will be represented that tackles this challenge in a two-step approach. During the first step, the recenly developed curvelet-based recovery by sparsity-promoting inversion (CRSI) is applied to the data, followed by a prediction of the primaries. During the second high-resolution step, the estimated primaries are used to improve the frequency content of the recovered data by combining the focal transform, defined in terms of the estimated primaries, with the curvelet transform. This focused curvelet transform leads to an improved recovery, which can subsequently be used as input for a second stage of multiple prediction and primary-multiple separation.

URLhttps://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/herrmann07SEGmpf/herrmann07SEGmpf.pdf
DOI10.1190/1.2792987
Presentation
Citation Keyherrmann2007SEGmpf