Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/325948
Type: Artigo
Title: Adaptive Multiple Subtraction: Unification And Comparison Of Matching Filters Based On The L(q)-norm And Statistical Independence
Adaptive multiple subtraction: unification and comparison of matching filters based on the l(q)-norm and statistical independence
Author: Batany, Yves-Marie
Duarte, Leonardo Tomazeli
Donno, Daniela
Romano, João Marcos Travassos
Chauris, Hervé
Abstract: An adaptive multiple subtraction step is necessary for almost all methods that predict seismic multiple reflected waves. We aim at giving a better understanding of matching filters based on l(q)-norms and on statistical independence. We found that the formulation of all of these techniques can be gathered in a mutual framework by introducing a space-time operator, called the primary enhancer, acting on the estimated primaries. The differences between the considered matching filters become more intuitive because this operator behaves as a simple amplitude compressor. In this perspective, all the methods tend to uncorrelate the predicted multiples and the enhanced estimated primaries. The study of these matching-filter methods can be narrowed to the study of the primary enhancer operator because it is the only difference. Moreover, we have emphasized the role of using adjacent traces or windowing approaches in terms of statistics, and we show that an adequate windowing strategy may overbear the choice of the objective function. Indeed, our analysis showed that setting a good windowing strategy may be more important than changing the classical least-squares adaptation criterion to other approaches based on l(q)-norm minimization or independent component analysis.
An adaptive multiple subtraction step is necessary for almost all methods that predict seismic multiple reflected waves. We aim at giving a better understanding of matching filters based on l(q)-norms and on statistical independence. We found that the for
Subject: Redes neurais (Ciência da computação)
Country: Estados Unidos
Editor: Society of Exploration Geophysicists
Citation: Geophysics. Soc Exploration Geophysicists, v. 81, p. V43 - V54, 2016.
Rights: aberto
fechado
Identifier DOI: 10.1190/GEO2015-0182.1
Address: http://library.seg.org/doi/abs/10.1190/geo2015-0182.1
Date Issue: 2016
Appears in Collections:FEEC - Artigos e Outros Documentos
FCA - Artigos e Outros Documentos

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