Generalized f‐k domain trace interpolation

Gülünay, Necati; Chambers, R.

doi:10.1190/1.1885582

Cited by 6 publications

(7 citation statements)

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“…Some methods to interpolate subsampled data with an underlying plane-wave assumption exist in the literature, such as Spitz's f-x method (Spitz 1991) and Gulunay's f-k method Gulunay and Chambers (1997). However, these methods require geophones to be regularly spaced (Abma and Kabir 2005), which is not always possible in practice.…”

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confidence: 98%

Application of randomized sampling schemes to curvelet‐based sparsity‐promoting seismic data recovery

Shahidi

Tang

et al. 2013

Geophysical Prospecting

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A B S T R A C TReconstruction of seismic data is routinely used to improve the quality and resolution of seismic data from incomplete acquired seismic recordings. Curvelet-based Recovery by Sparsity-promoting Inversion, adapted from the recently-developed theory of compressive sensing, is one such kind of reconstruction, especially good for recovery of undersampled seismic data. Like traditional Fourier-based methods, it performs best when used in conjunction with randomized subsampling, which converts aliases from the usual regular periodic subsampling into easy-to-eliminate noise. By virtue of its ability to control gap size, along with the random and irregular nature of its sampling pattern, jittered (sub)sampling is one proven method that has been used successfully for the determination of geophone positions along a seismic line. In this paper, we extend jittered sampling to two-dimensional acquisition design, a more difficult problem, with both underlying Cartesian and hexagonal grids. We also study what we term separable and non-separable two-dimensional jittered samplings. We find hexagonal jittered sampling performs better than Cartesian jittered sampling, while fully non-separable jittered sampling performs better than separable jittered sampling. Two other 2D randomized sampling methods, Poisson Disk sampling and Farthest Point sampling, both known to possess blue-noise spectra, are also shown to perform well.

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confidence: 98%

Application of randomized sampling schemes to curvelet‐based sparsity‐promoting seismic data recovery

Shahidi

Tang

et al. 2013

Geophysical Prospecting

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“…7 After this stage the source and receivers were equally sampled at an interval small enough to avoid aliasing problems. This step made the shot interval an integer multiple of the receiver interval.…”

Section: Processing Methodologymentioning

confidence: 99%

3-D Surface Multiple Attenuation

Dragoset¹

2000

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Surface multiples were removed from an irregularly sampled Gulf of Mexico 3-D seismic data set using a 2-D wave equation-based algorithm. Accomplishing that task required facing several challenges: the project's potentially high processing cost, the 2-D algorithm's inability to accommodate 3-D effects directly, and the algorithm's need to have an input wavefield with regular spatial sampling. The first two challenges were addressed by predicting surface multiples in a 2-D fashion for half of the 2-D lines in the 3-D data set. The predicted multiples were then subtracted from the original lines using a process called adaptive noise cancellation to correct the predictions for ignored 3-D effects such as those due to cable feathering and cross dip. In addition, the adaptive process was able to subtract multiples predicted for one subsurface line not only from that line, but also from an adjacent line, thereby almost halving the cost of the project. The spatial sampling problem, which arose mostly because the survey was designed without surface multiple prediction in mind, was solved by regularizing the 2-D lines with an assortment of interpolation processes before multiple prediction. The interpolation increased the processing cost by about a factor of four.

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“…From a practical point of view, the most important seismic trace interpolation methods for spatially aliased data are the ω‐ x domain trace interpolation method (Spitz 1991) and the ω‐ k domain trace interpolation method (Gulunay and Chambers 1996, 1997; Guo, Zhou and Yang 1996). Both methods assume that in each processing gate there is a limited number of linear events and solve the difficult problem of finding the dips of these events.…”

Section: Trace Interpolation Of Spatially Aliased Seismic Datamentioning

confidence: 99%

The Petersen‐Middleton theorem and sampling of seismic data

Bardan

2009

Geophysical Prospecting

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A B S T R A C TThe Petersen-Middleton sampling theorem can lead to weaker sampling requirements than the Shannon sampling theorem and therefore proposes advantageous economic alternatives for the acquisition and processing of seismic data. In this paper we present a tutorial review on the Petersen-Middleton sampling theorem and a unified description of its applications to seismic survey: the interpolation of spatially aliased seismic data, the dealiasing of seismic common midpoint and common offset gathers, the recording and processing of seismic data on a hexagonal sampling grid. The important aspects of this sampling theorem are highlighted and illustrated by synthetic examples.

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Generalized f‐k domain trace interpolation

Cited by 6 publications

References 0 publications

Application of randomized sampling schemes to curvelet‐based sparsity‐promoting seismic data recovery

Application of randomized sampling schemes to curvelet‐based sparsity‐promoting seismic data recovery

3-D Surface Multiple Attenuation

The Petersen‐Middleton theorem and sampling of seismic data

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