3D SRME Practice for Better Imaging

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

doi:10.3997/2214-4609-pdb.1.a030

Cited by 7 publications

(1 citation statement)

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“…The field data that we used for testing our approach is from a 3D BroadSeis survey conducted by CGG for Shell Brunei in 2012, which covers an area of 3100 km 2 (Soubaras and Dowle, 2010). 3D SRME was applied to predict surfacerelated multiples for this project (Lin et al, 2005). Noticing that the multiple model has a certain degree of discrepancy in terms of frequency content and the data are contaminated with high-frequency noise, we applied FrACDS to provide more flexibility in solving these difficulties.…”

Section: Synthetic and Field Data Examplesmentioning

confidence: 99%

High-fidelity Adaptive Curvelet Domain Primary-Multiple Separation

Wu¹,

Hung²

2013

London 2013, 75th Eage Conference en Exhibition Incorporating SPE Europec

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Section: Synthetic and Field Data Examplesmentioning

confidence: 99%

High-fidelity Adaptive Curvelet Domain Primary-Multiple Separation

Wu¹,

Hung²

2013

London 2013, 75th Eage Conference en Exhibition Incorporating SPE Europec

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True-azimuth 3D Internal Multiple Attenuation without identifying the multiple generators

Hung¹,

Wang²,

Griffiths

2013

ASEG Extended Abstracts

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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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3D SRME Practice for Better Imaging

Cited by 7 publications

References 0 publications

High-fidelity Adaptive Curvelet Domain Primary-Multiple Separation

High-fidelity Adaptive Curvelet Domain Primary-Multiple Separation

True-azimuth 3D Internal Multiple Attenuation without identifying the multiple generators

A perspective on 3D surface-related multiple elimination

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