3D SRME prediction and subtraction practice for better imaging

Lin, Dechun; Young, Jerry; Lin, Wen-jack; Griffiths, Malcolm; Hartmann, Monica

doi:10.1190/1.2148123

Cited by 13 publications

(6 citation statements)

References 6 publications

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“…where P 0 = D, and the operator is the so-called adaptive subtraction (Guo, 2001) which is required because the predicted multiple model carries an extra source signature and amplitude errors due to cross-talk. The first iteration P 1 usually gives respectable results when the seismic wave-field is reasonably well sampled and the water depth is deep (Lin et al, 2005). However, as the water depth decreases, M 1 (and thus P 1 ) can become inadequate to the point that the iteration depicted in equation 2 cannot converge to a satisfactory result.…”

Section: Cross-talk Among Different Orders Of Multiplesmentioning

confidence: 99%

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A model-based water-layer demultiple algorithm

Wang¹,

Jin²,

Yang³

et al. 2014

First Break

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This paper focuses on the attenuation of Water-Layer-Related Multiples (WLRMs or peg-leg multiples) which reflect at least once between the water bottom and the water surface. WLRMs are often the most dominant multiples in shallow-water seismic data. We propose a Model-based Water-layer Demultiple (MWD) algorithm to calculate the Green's functions of the Water-Layer Primary Reflections (WLPRs: Green's functions convolved with source signature) based on the known seabed and water-layer velocity model and then convolve them with the recorded data to predict the WLRMs. Combined with adaptive subtraction, MWD can effectively attenuate WLRMs. We apply MWD to field data from the Hibernia oilfield area which has a water depth of 70-90 m. The results show that while Surface-Related Multiple Elimination (SRME) by itself has limited success, MWD is effective in attacking WLRMs. Once the WLRMs have been removed by MWD, successive SRME can then be applied to predict and eliminate other types of surface-related multiples (SRMs). The combination of MWD and SRME is demonstrated as an effective multiple attenuation package for shallow-water data and results in fewer residual multiples and better preserved primaries over tau-p gapped deconvolution. This, in turn, contributes to a more realistic velocity model and higher-quality images.

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Section: Cross-talk Among Different Orders Of Multiplesmentioning

confidence: 99%

“…We tested both 3D SRME (Lin et al, 2005) and 3D MWD (Wang et al, 2011) to predict multiple model for the same 2D dataset using a same 3D prediction cube. The results are shown in Figure 5 as a 2D post-stack time migration.…”

Section: Application To Synthetic Datamentioning

confidence: 99%

A model-based water-layer demultiple algorithm

Wang¹,

Jin²,

Yang³

et al. 2014

First Break

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“…The most important shortcoming in practice is inadequate sampling, in terms of sampling density and range of offsets acquired. Many workers ͑e.g., Lin et al, 2005;Bisley and MacNeill, 2008;McHugo et al, 2009͒ show the detrimental effects of insufficient aperture and sampling on multiple models using field data.…”

Section: Application Of 3d Srme To Field Datamentioning

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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“…65, No. 3 (2012) (Lin et al, 2005;Bisley, 2008;Dragoset et al, 2010 (Hampson, 1987 (Sacchi, 1999;Trad et al, 2002 1-5-21, Otsuka, Bunkyo-ku, Tokyo 112-0012, Japan ** Japan Oil, Gas and Metals National Corporation 1-2-2, Hamada, Mihama-ku, Chiba 261-0025, Japan A part of this paper was presented at the 123rd SEGJ fall conference, 2010.…”

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confidence: 99%

Multiple attenuation using 3D SRME<br/>—Optimization of Multiple Contribution Gather—

加藤¹,

猪野²,

阿部³

et al. 2012

BUTSURI-TANSA(Geophysical Exploration)

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This paper presents an optimization of multiple attenuation including three-dimensional Surface-Related Multiple Elimination (3D SRME). First of all, using marine 3D seismic data, we tested 3D SRME in terms of interval distance of the Multiple Contribution Gather (MCG) and the aperture parameter. The aperture parameter defines the ellipsoidal area for producing MCG. It is found that the optimal MCG interval is same as CMP interval and the aperture parameter should be set to the length that the ellipsoidal area includes neighboring sail lines. Moreover, the strategy of the aperture parameter presented in this paper will offer an optimized quality of 3D SRME. Subsequently, we conducted the comparison of 3D SRME and PRT. According to our results, 3D SRME performed better than PRT in near-to middle-offset records and effectively worked under circumstances where the diffracted multiple was observed or the difference of RMS velocity between primary and multiple reflection was small. Moreover, 3D SRME is expected to contribute to increase the accuracy of such cascaded processes as deconvolution or velocity analysis since it can be applied to the field-data. On the other hand, PRT showed superiority to 3D SRME on the far-offset records; therefore, it is considered advantageous for deep subsurface imaging. In conclusion, the implementation of PRT after 3D SRME can produce the best results owing to complementary attributes of each method.

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3D SRME prediction and subtraction practice for better imaging

Cited by 13 publications

References 6 publications

A model-based water-layer demultiple algorithm

A model-based water-layer demultiple algorithm

A perspective on 3D surface-related multiple elimination

Multiple attenuation using 3D SRME<br/>—Optimization of Multiple Contribution Gather—

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3D SRME prediction and subtraction practice for better imaging

Cited by 13 publications

References 6 publications

A model-based water-layer demultiple algorithm

A model-based water-layer demultiple algorithm

A perspective on 3D surface-related multiple elimination

Multiple attenuation using 3D SRME<br/>&mdash;Optimization of Multiple Contribution Gather&mdash;

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Multiple attenuation using 3D SRME<br/>—Optimization of Multiple Contribution Gather—