Wavefield de‐aliasing for acquisition configurations leading to coarse sampling

Manin, Michel; Spitz, Simon

doi:10.1190/1.1887527

Cited by 7 publications

(3 citation statements)

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“…Impressive examples are shown in Wombell and Williams (1995) and Manin and Spitz (1995). Nevertheless, apart from low fold, another reason not to use more than two sources in modern MS/MS configurations is to prevent or limit multiple aliasing.…”

Section: Source Interval Number Of Sources and Foldmentioning

confidence: 96%

“…Offset sampling in a CMP is 2 × source interval, so aliasing of multiples in midpoints (Section 3.4.3.2) is less likely to occur for single-source than for dual-source data. In particular, multiples with large differential moveout with respect to the primaries may be severely undersampled in the CMPs, even after NMO correction (Hobson et al, 1992;Manin and Spitz, 1995;Wombell and Williams, 1995;Hegna and Schoolmeesters, 2001;.…”

Section: Source Interval Number Of Sources and Foldmentioning

confidence: 99%

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3D Seismic Survey Design, Second edition

Vermeer¹

2012

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Section: Source Interval Number Of Sources and Foldmentioning

confidence: 96%

Section: Source Interval Number Of Sources and Foldmentioning

confidence: 99%

3D Seismic Survey Design, Second edition

Vermeer¹

2012

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“…Interpolation of correctly sampled data is almost trivial, yet it is not so in the presence of aliasing. Methods of trace interpolation include sinc interpolation (Jakubowicz, 1994(Jakubowicz, , 1997 after alias reduction via NMO, most coherent dip interpolation (Larner et al, 1981), semblance weighted slant-stack interpolation (Lu, 1985), interpolation using event attributes (King et al, 1984), power diversity slant-stack interpolation (Monk et al, 1993), Radon domain trace interpolation for irregularly sampled or missing data (Kostov, 1989;Darche, 1990;Kabir and Verschuur, 1992;Schonewille and Duijndam, 1996), f -x prediction filter interpolation (Spitz, 1989(Spitz, , 1991Ji, 1993;Manin and Spitz, 1995;Porsani, 1999), f -x projection filter interpolation (Soubaras, 1997), t-x domain prediction error filter (PEF) interpolation (Claerbout and Nichols, 1991;Claerbout, 1992), and others combining different domains, such as f -x domain wavefield decomposition using a picked dip field (Pieprzak andMcClean, 1988, 1990). Trace interpolation methods that make use of the frequency-wavenumber ( f -k) domain have also emerged (Pan and Fields, 1986;Guo et al, 1996;Gülünay and Chambers, 1996).…”

Section: Introductionmentioning

confidence: 99%

Seismic trace interpolation in the Fourier transform domain

Gülünay¹

2003

GEOPHYSICS

197

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A data adaptive interpolation method is designed and applied in the Fourier transform domain (f‐k or f‐kx‐ky for spatially aliased data. The method makes use of fast Fourier transforms and their cyclic properties, thereby offering a significant cost advantage over other techniques that interpolate aliased data. The algorithm designs and applies interpolation operators in the f‐k (or f‐kx‐ky domain to fill zero traces inserted in the data in the t‐x (or t‐x‐y) domain at locations where interpolated traces are needed. The interpolation operator is designed by manipulating the lower frequency components of the stretched transforms of the original data. This operator is derived assuming that it is the same operator that fills periodically zeroed traces of the original data but at the lower frequencies, and corresponds to the f‐k (or f‐kx‐ky domain version of the well‐known f‐x (or f‐x‐y) domain trace interpolators. The method is applicable to 2D and 3D data recorded sparsely in a horizontal plane. The most common prestack applications of the algorithm are common‐mid‐point domain shot interpolation, source‐receiver domain shot interpolation, and cable interpolation.

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A Study on Reconstruction of De‐Aliased Uneven Seismic Data

Liu

2004

Chinese J of Geophysics

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Seismic data spatial trace interpolation is one of the most important issues in seismic data processing. In this paper, a novel Fourier transform based algorithm is proposed, which can reconstruct both uneven and aliased seismic data. We formulate band‐limited data reconstruction as a minimum norm least squares inverse problem where an adaptive DFT‐weighted norm regularization term is used. The inverse problem is solved by the preconditioned conjugate gradient algorithm, which makes the solutions stable and convergence quick. Based on the assumption that local seismic data are consisted of finite linear events, from the sampling theorem, aliased events can be attenuated via LS weighting prediction linearly from low frequency. Three application cases are discussed on even gap trace interpolation, uneven gap filling and high frequency trace reconstruction from low frequency data traces constrained by a few high frequency traces. Both synthetic and real data numerical examples show that the proposed method is valid, efficient and applicable. This research is valuable to seismic data regularization and cross well seismic exploration.

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Wavefield de‐aliasing for acquisition configurations leading to coarse sampling

Cited by 7 publications

References 0 publications

3D Seismic Survey Design, Second edition

3D Seismic Survey Design, Second edition

Seismic trace interpolation in the Fourier transform domain

A Study on Reconstruction of De‐Aliased Uneven Seismic Data

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