Adaptive separation of free-surface multiples through independent component analysis

Kaplan, Sam T.; Innanen, Kristopher A.

doi:10.1190/1.2890407

Cited by 47 publications

(15 citation statements)

References 18 publications

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“…Pham et al (2014) also propose a more general framework able to introduce different kinds of norms for the estimated primaries and the filter. More recently, some authors consider that primaries and multiples can be modeled as statistical independent variables (Kaplan and Innanen, 2008;Donno, 2011;Li and Lu, 2013). As in equation 3, we can write the optimization problem as find w such thatp and m ̮ are independent:…”

Section: Objective Functions Used For Adaptive Multiples Subtractionmentioning

confidence: 99%

“…This approach has led to the use of new objective functions associated with methods such as geometric-based ICA (Lu, 2006), FastICA (Kaplan and Innanen, 2008), kurtosis-based methods (Donno, 2011), InfoMax (Liu and Dragoset, 2013), and negentropy maximization (Li and Lu, 2013). The first works (Lu, 2006;Kaplan and Innanen, 2008;Donno, 2011) on ICA-based adaptive multiple subtraction operate in a two-step fashion. They comprise an estimation of the shape of the filter using a classic l 2 -norm matching filter or a histogram method to correct for time delay, followed by a more precise adjustment of its amplitude using ICA.…”

Section: Introductionmentioning

confidence: 98%

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Adaptive multiple subtraction: Unification and comparison of matching filters based on the ℓq-norm and statistical independence

et al. 2016

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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 leastsquares adaptation criterion to other approaches based on l q -norm minimization or independent component analysis.

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Section: Objective Functions Used For Adaptive Multiples Subtractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Adaptive multiple subtraction: Unification and comparison of matching filters based on the ℓq-norm and statistical independence

et al. 2016

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“…Em [17] mostrou-se o excelente desempenho das técnicas de ICA no problema de separação entre os chamados eventos alinhados e não-alinhados. Outros traba-lhos consideram ainda a aplicação de BSS para a subtração adaptativa de múltiplas [18], [19].…”

Section: B Separação De Fontes Por Componentes Esparsas (Sca)unclassified

Processamento Digital de Sinais Geofísicos

Romano¹,

Lópes²,

Tygel³

2011

Anais De XXIX Simpósio Brasileiro De Telecomunicações

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Resumo-Diversas atividades, como a exploração e o monitoramento de reservatórios de petróleo, requerem o conhecimento de propriedades da subsuperfície da região de interesse. A determinação destas propriedadesé o objetivo do processamento sísmico. Diversos métodos de processamento sísmico guardam grande semelhança com aqueles usados pela comunidade de processamento digital de sinais, sendo que, mais recentemente, observa-se uma clara tendência em re-estreitar os laços, historicamente fortes, entre essas as duas comunidades. Neste contexto, pretendemos com este trabalho descrever algumas técnicas de processamento sísmico, destacando problemas cuja solução já se beneficia, ou pode se beneficiar, de métodos processamento de sinais. Nosso principal objetivoé mostrar a sinergia existente entre as duasáreas, enfatizando as diversas perspectivas de pesquisa, bastante promissoras para ambas as comunidades. Palavras-Chave-ProcessamentoSísmico, Desconvolução Cega, Separação Cega de Fontes.Abstract-Several applications, such as exploration and monitoring of oil reserves, rely on some knowledge of properties of the subsurface. Estimating these properties is the main goal of seismic processing. Several seismic processing methods bear great resemblance to those traditionally used by the signal processing community. Recently, there is a clear trend in re-establishing the historically strong ties among these two communities. In this context, this work describes some methods of seismic processing, highlighting problems whose solution benefits, or may benefit, from signal processing techniques. Our main goal is to emphasize the synergy between these research areas, and to identify several research topics that are promising for both.

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“…An improved method based on the l 1 norm is also proposed by Pang and Lu (2009). Independent component analysis (ICA)-based methods for adaptive multiple subtraction are used in recent literature (Lu and Mao, 2005;Lu, 2006;Kaplan and Innanen, 2008;Lu and Liu, 2009). Pattern-based methods (Spitz, 1999;Guitton, 2005) and a 3D-based method (Li and Lu, 2013) have also been developed.…”

Section: Introductionmentioning

confidence: 98%

Adaptive multiple subtraction based on multiband pattern coding

Liu

2016

GEOPHYSICS

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Adaptive multiple subtraction is the key step of surfacerelated multiple elimination methods. The main challenge of this technique resides in removing multiples without distorting primaries. We have developed a new pattern-based method for adaptive multiple subtraction with the consideration that primaries can be better protected if the multiples are differentiated from the primaries. Different from previously proposed methods, our method casts the adaptive multiple subtraction problem as a pattern coding and decoding process. We set out to learn distinguishable patterns from the predicted multiples before estimating the multiples contained in seismic data. Hence, in our method, we first carried out pattern coding of the predicted multiples to learn the special patterns of the multiples within different frequency bands. This coding process aims at exploiting the key patterns contained in the predicted multiples. The learned patterns are then used to decode (extract) the multiples contained in the seismic data, in which process those patterns that are similar to the learned patterns were identified and extracted. Because the learned patterns are obtained from the predicted multiples only, our method avoids the interferences of primaries in nature and shows an impressive capability for removing multiples without distorting the primaries. Our applications on synthetic and real data sets gave some promising results.

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Adaptive separation of free-surface multiples through independent component analysis

Cited by 47 publications

References 18 publications

Adaptive multiple subtraction: Unification and comparison of matching filters based on the ℓq-norm and statistical independence

Adaptive multiple subtraction: Unification and comparison of matching filters based on the ℓq-norm and statistical independence

Processamento Digital de Sinais Geofísicos

Adaptive multiple subtraction based on multiband pattern coding

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