Restoration of missing offsets by parabolic Radon transform<sup>1</sup>

Kabir, Md. Monirul; Verschuur, D.J.

doi:10.1111/j.1365-2478.1995.tb00257.x

Cited by 167 publications

(70 citation statements)

References 17 publications

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“…The time sampling interval is 8ms. Interpolation and the reciprocity principle were used in order to reconstruct the near offset data (Kabir and Verschuur, 1995). Surface-related multiples were also removed.…”

Section: Methods and Theorymentioning

confidence: 99%

Acquisition Geometry-aware Focal Deblending

Kontakis¹,

Wu²,

Verschuur³

2016

78th EAGE Conference and Exhibition 2016

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SUMMARYThe applicability of a deblending method is directly related to acquisition parameters, such as source and detector locations. We formulate focal deblending in two alternative ways. In the first case, the double focal transform is used, which relies on a well-sampled source and detector dimension. In the second case, the single-sided focal transform is used, which does not depend on a well-sampled source dimension. Comparing the deblending results, we find that although the double focal transform is superior, blending noise can be significantly attenuated using the single-sided focal transform, which allows application to more practical acquisition geometries. By combining with shot repetition, the deblending result can be further improved.

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Section: Methods and Theorymentioning

confidence: 99%

Acquisition Geometry-aware Focal Deblending

Kontakis¹,

Wu²,

Verschuur³

2016

78th EAGE Conference and Exhibition 2016

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“…However, at small offsets -where the nearest offset typically is 100-200 m -this produces severe edge effects in the predicted multiples, especially in shallow-water situations ͑see, e.g., Verschuur, 1991Verschuur, , 2006Dragoset, 1993͒. Therefore, a very important preprocessing step is to extend the offsets to zero offset ͑see, e.g., Kabir and Verschuur, 1995͒ and even beyond by applying reciprocity.…”

Section: Missing Near-offset Reconstructionmentioning

confidence: 99%

A perspective on 3D surface-related multiple elimination

et al. 2010

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Surface-related multiple elimination ͑SRME͒ is an algorithm that predicts all surface multiples by a convolutional process applied to seismic field data. Only minimal preprocessing is required. Once predicted, the multiples are removed from the data by adaptive subtraction. Unlike other methods of multiple attenuation, SRME does not rely on assumptions or knowledge about the subsurface, nor does it use event properties to discriminate between multiples and primaries. In exchange for this "freedom from the subsurface," SRME requires knowledge of the acquisition wavelet and a dense spatial distribution of sources and receivers. Although a 2D version of SRME sometimes suffices, most field data sets require 3D SRME for accurate multiple prediction. All implementations of 3D SRME face a serious challenge: The sparse spatial distribution of sources and receivers available in typical seismic field data sets does not conform to the algorithmic requirements. There are several approaches to implementing 3D SRME that address the data sparseness problem. Among those approaches are pre-SRME data interpolation, on-the-fly data interpolation, zero-azimuth SRME, and trueazimuth SRME. Field data examples confirm that ͑1͒ multiples predicted using true-azimuth 3D SRME are more accurate than those using zero-azimuth 3D SRME and ͑2͒ on-thefly interpolation produces excellent results.

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“…Alternatively, we can use equation (15) as in the conventional SMA, in which we need to extrapolate live traces to fill the near-offset gap (Kabir and Verschuur, 1995). The examples shown in this paper use equation (15), in which a simple moveout correction method projects the first live trace to the offset locations within the gap.…”

Section: Application Examplesmentioning

confidence: 99%

Multiple prediction through inversion: A fully data‐driven concept for surface‐related multiple attenuation

Wang

2004

GEOPHYSICS

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This paper introduces a fully data-driven concept, multiple prediction through inversion (MPI), for surfacerelated multiple attenuation (SMA). It builds the multiple model not by spatial convolution, as in a conventional SMA, but by updating the attenuated multiple wavefield in the previous iteration to generate a multiple prediction for the new iteration, as is usually the case in an iterative inverse problem. Because MPI does not use spatial convolution, it is able to minimize the edge effect that appears in conventional SMA multiple prediction and to eliminate the need to synthesize near-offset traces, required by a conventional scheme, so that it can deal with a seismic data set with missing near-offset traces. The MPI concept also eliminates the need for an explicit surface operator, which is required by conventional SMA and is comprised of the inverse source signature and other effects. This method accounts implicitly for the spatial variation of the surface operator in multiple-model building and attempts to predict multiples which are not only accurate kinematically but are also accurate in phase and amplitude.

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Restoration of missing offsets by parabolic Radon transform¹

Cited by 167 publications

References 17 publications

Acquisition Geometry-aware Focal Deblending

Acquisition Geometry-aware Focal Deblending

A perspective on 3D surface-related multiple elimination

Multiple prediction through inversion: A fully data‐driven concept for surface‐related multiple attenuation

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Restoration of missing offsets by parabolic Radon transform1

Cited by 167 publications

References 17 publications

Acquisition Geometry-aware Focal Deblending

Acquisition Geometry-aware Focal Deblending

A perspective on 3D surface-related multiple elimination

Multiple prediction through inversion: A fully data‐driven concept for surface‐related multiple attenuation

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Restoration of missing offsets by parabolic Radon transform¹