True‐azimuth versus zero‐azimuth 3‐D multiple prediction in WATS processing

Aaron, Peter; O’Toole, Rebecca J.; Barnes, Simon; Borselen, Roald van; Hegge, Rob

doi:10.1190/1.3059367

Cited by 12 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data are from a WAZ survey in the Keathley Canyon area. For more information on this data set, see Aaron et al ͑2008͒ andFromyr et al ͑2008͒. For more information on this data set, see Aaron et al ͑2008͒ andFromyr et al ͑2008͒.…”

Section: Gulf Of Mexico Wide-azimuth Data Examplementioning

confidence: 99%

See 1 more Smart Citation

20. Signal Processing

Zwartjes¹,

Sacchi²

2010

Geophysics Today

View full text Add to dashboard Cite

There are numerous methods for interpolating uniformly sampled, aliased seismic data, but few can handle the combination of nonuniform sampling and aliasing. We combine the principles of Fourier reconstruction of nonaliased, nonuniformly sampled data with the ideas of frequency-wavenumber ͑ f-k͒ interpolation of aliased, uniformly sampled data in a new two-step algorithm. In the first step, we estimate the Fourier coefficients at the lower nonaliased temporal frequencies from the nonuniformly sampled data. The coefficients are then used in the second step as an a priori model to distinguish between aliased and nonaliased energy at the higher, aliased temporal frequencies. By using a nonquadratic model penalty in the inversion, both the artifacts in the Fourier domain from nonuniform sampling and the aliased energy are suppressed. The underlying assumption is that events are planar; therefore, the algorithm is applied to seismic data in overlapping spatiotemporal windows.

show abstract

Section: Gulf Of Mexico Wide-azimuth Data Examplementioning

confidence: 99%

“…͑fromAaron et al, 2008͒ Note the errors in predicted multiples as observed in the zeroazimuth prediction ͑within the red ovals͒ are absent in the true-azimuth prediction ͑within the green ovals͒.…”

mentioning

confidence: 99%

20. Signal Processing

Zwartjes¹,

Sacchi²

2010

Geophysics Today

View full text Add to dashboard Cite

show abstract

“…The shot record shown in Figure 14a is located at a 2.4-km nominal crossline offset. For more information on this data set, see Aaron et al ͑2008͒ andFromyr et al ͑2008͒. Figure 14b shows the predicted multiples when the zero-azimuth assumption is used, meaning that the input data are first transformed to zero azimuth via an azimuth-moveout procedure, after which a zero-azimuth prediction takes place, according to Matson and Abma ͑2005͒.…”

Section: Gulf Of Mexico Wide-azimuth Data Examplementioning

confidence: 99%

A perspective on 3D surface-related multiple elimination

et al. 2010

View full text Add to dashboard Cite

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.

show abstract

“…However, usually the wavelet effects are kept in, yielding predicted multiples that exhibit a wrong wavelet, which needs to be corrected for in adaptive subtraction. The latter is usually based on a minimum energy criterion, which is known for not always being optimal for SRME (Nekut and Verschuur, 1998;Guitton and Verschuur, 2004;Aaron et al, 2008;Dragoset et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Integrated receiver deghosting and closed-loop surface-multiple elimination

2017

View full text Add to dashboard Cite

Accurate surface-related multiple removal is an important step in conventional seismic processing, and more recently, primaries and surface multiples are separated such that each of them is available for imaging algorithms. Current developments in the field of surface-multiple removal aim at estimating primaries in a large-scale inversion process. Using such a so-called closed-loop process, in each iteration primaries and surface multiples will be updated until they fit the measured data. The advantage of redefining surface-multiple removal as a closed-loop process is that certain preprocessing steps can be included, which can lead to an improved multiple removal. In principle, the surface-related multiple elimination process requires deghosted data as input; thus, the source and receiver ghost must be removed. We have focused on the receiver ghost effect and assume that the source is towed close to the sea surface, such that the source ghost effect is well-represented by a dipole source. The receiver ghost effect is integrated within the closed-loop primary estimation process. Thus, primaries are directly estimated without the receiver ghost effect. After receiver deghosting, the upgoing wavefield is defined at zero depth, which is the surface. We have successfully validated our method on a 2D simulated data and on a 2D subset from 3D broadband field data with a slanted cable.

show abstract

True‐azimuth versus zero‐azimuth 3‐D multiple prediction in WATS processing

Cited by 12 publications

References 4 publications

20. Signal Processing

20. Signal Processing

A perspective on 3D surface-related multiple elimination

Integrated receiver deghosting and closed-loop surface-multiple elimination

Contact Info

Product

Resources

About