Surface-related multiple elimination on wide-tow marine data

The presence of multiple reflections is common in marine surveys due to the air-water interface. Multiples have significant energy and can mask deep reflectors, leading to the misinterpretation of seismic cross-sections. In this study, surface-related multiple elimination (SRME), predictive deconvolution in the domain τ − p domain and Radon and f − k filtering are used to eliminate surface multiples in real 2D marine data. These methods are applied in different combinations, and the results are analyzed with the aim of determining an optimal seismic processing flow for the removal of surface multiples. RESUMO: No levantamento marinho é comum a presença de reflexões múltiplas devido à interface ar-água. Essas reflexões múltiplas possuem energia considerável e podem mascarar reflexões primárias levando a erros de interpretação da seção sísmica. Neste trabalho é determinado um fluxo ótimo de processamento sísmico para atenuação de múltiplas de superfície. Os métodos de eliminação de múltiplas de superfície (SRME), deconvolução preditiva no domínio τ − p e as filtragens Radone f − k são aplicados a um dado marinho real 2D em diferentes combinações. Os resultados são analisados com objetivo de determinar um fluxo de processamento sísmico ótimo para atenuação de múltiplas de superfície.Palavras-chave: atenuação de múltiplas de superfície; SRME; filtragem Radon; deconvolução preditiva no domínio τ − p; filtragem f − k

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3D surface-related multiple elimination: Data reconstruction and application to field data

Geophysics ◽

10.1190/1.2194515 ◽

2006 ◽

Vol 71 (3) ◽

pp. E25-E33 ◽

Cited By ~ 7

Author(s):

Anatoly Baumstein ◽

Mohamed T. Hadidi

Keyword(s):

Field Data ◽

Higher Order ◽

Three Dimensions ◽

Approximate Algorithm ◽

Data Reconstruction ◽

Reconstruction Procedure ◽

The Common ◽

Marine Data ◽

Multiple Prediction ◽

Multiple Elimination

The wide success of 2D surface-related multiple elimination (SRME) in attenuating complex multiples in many cases has spurred efforts to apply the method in three dimensions. However, application of 3D SRME to conventional marine data is often impeded by severe crossline aliasing characteristic of marine acquisition geometries. We propose to overcome this limitation using a dip-moveout (DMO)-based procedure consisting of the following steps: resorting the data into common offsets to improve crossline sampling, performing DMO to eliminate azimuth variations in the common-offset domain, and efficiently implementing inverse shot-record DMO to reconstruct densely sampled shot records required for 3D SRME to predict multiples correctly. We use a field data example to demonstrate that the proposed shot reconstruction procedure leads to kinematically accurate reconstruction of primaries but may not be able to simultaneously position multiples correctly. The mispositioning of multiples becomes a problem when second- and higher-order multiples must be predicted. We propose to resolve this difficulty by using a layer-stripping approach to multiple prediction. Alternatively, an approximate algorithm that relies on adaptive subtraction to compensate for inaccurate positioning of predicted multiples can be used. Application of the latter approach is illustrated with a field data example, and its performance is evaluated quantitatively through a measurement of S/N ratio improvement. We demonstrate that a DMO-based implementation of 3D SRME outperforms conventional 2D SRME and can accurately predict and attenuate complex 3D multiples.

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3D surface-related multiple prediction: A sparse inversion approach

Geophysics ◽

10.1190/1.1925752 ◽

2005 ◽

Vol 70 (3) ◽

pp. V31-V43 ◽

Cited By ~ 34

Author(s):

E. J. van Dedem ◽

D. J. Verschuur

Keyword(s):

Input Data ◽

Synthetic Data ◽

Data Set ◽

3D Surface ◽

Parametric Inversion ◽

Sparse Inversion ◽

Marine Data ◽

Multiple Prediction ◽

Multiple Elimination

The theory of iterative surface-related multiple elimination holds for 2D as well as 3D wavefields. The 3D prediction of surface multiples, however, requires a dense and extended distribution of sources and receivers at the surface. Since current 3D marine acquisition geometries are very sparsely sampled in the crossline direction, the direct Fresnel summation of the multiple contributions, calculated for those surface positions at which a source and a receiver are present, cannot be applied without introducing severe aliasing effects. In this newly proposed method, the regular Fresnel summation is applied to the contributions in the densely sampled inline direction, but the crossline Fresnel summation is replaced with a sparse parametric inversion. With this procedure, 3D multiples can be predicted using the available input data. The proposed method is demonstrated on a 3D synthetic data set as well as on a 3D marine data set from offshore Norway.

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