An analysis of 2D and 3D multiple attenuation for a canonical example

S U M M A R YWe here describe one way of constructing internal multiples from surface seismic data only. The key feature of our construct of internal multiples is the introduction of the concept of virtual seismic events. Virtual events here are events, which are not directly recorded in standard seismic data acquisition, but their existence allows us to construct internal multiples with scattering points at the sea surface; the standard construct of internal multiples does not include any scattering points at the sea surface.The mathematical and computational operations invoked in our construction of virtual events and internal multiples are similar to those encountered in the construction of free-surface multiples based on the Kirchhoff or Born scattering theory. For instance, our construct operates on one temporal frequency at a time, just like free-surface demultiple algorithms; other internal multiple constructs tend to require all frequencies for the computation of an internal multiple at a given frequency. It does not require any knowledge of the subsurface nor an explicit knowledge of specific interfaces that are responsible for the generation of internal multiples in seismic data. However, our construct requires that the data be divided into two, three or four windows to avoid generating primaries. This segmentation of the data also allows us to select a range of periods of internal multiples that one wishes to construct because, in the context of the attenuation of internal multiples, it is important to avoid generating short-period internal multiples that may constructively average to form primaries at the seismic scale.

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Adaptive multiple subtraction based on 3D blind separation of convolved mixtures

2013

GEOPHYSICS

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Multiple removal is one of the important preprocessing steps in a seismic data processing sequence. For an accurate multiple prediction, a full 3D method is required. However, the application of such methods is often limited by practical and economic constraints. Therefore, in practical situations, 2D prediction methods are used, such as 2D surface-related multiple elimination. However, the resulting predicted multiples may have significant temporal shift, spatial mismatch, and amplitude inconsistency, compared with true 3D multiples. Adaptive multiple subtraction based on 2D blind separation of convolved mixtures (BSCM) has been proposed to estimate the 2D matching filter in a single gather. To improve the flexibility of the adaptive multiple subtraction for the inconsistencies between the 2D predicted multiples and true 3D multiples, we evaluated the adaptive multiple subtraction as a problem of 3D BSCM. In the proposed method, the predicted multiples were modeled as the convolution of the true multiples with a 3D kernel, whose third dimension is in the gather direction. By maximizing the non-Gaussianity of the estimated primaries, the iterative reweighted least-squares algorithm was exploited to obtain the 3D matching filter, which is the inverse of the 3D kernel. To avoid the possible overfitting problem introduced by the 3D matching filter, the proposed method fit several seismic gathers using one 3D matching filter. In addition, by using the non-Gaussian maximization criterion, the proposed method alleviated the orthogonality assumption used by the least-squares subtraction method. Furthermore, the proposed method can eliminate the temporal and spatial mismatches between the 2D predicted multiples and true 3D multiples better than the 2D BSCM subtraction method. Tests on synthetic and field data sets demonstrated the effectiveness of the 3D BSCM subtraction method.

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An analysis of 2D and 3D multiple attenuation for a canonical example

Cited by 3 publications

References 23 publications

Increasing signal-to-noise ratio of marine seismic data: A case study from offshore Korea

Increasing signal-to-noise ratio of marine seismic data: A case study from offshore Korea

A construct of internal multiples from surface data only: the concept of virtual seismic events

Adaptive multiple subtraction based on 3D blind separation of convolved mixtures

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