3D joint deghosting and crossline interpolation for marine single-component streamer data

Ray, Suryadeep; Nimsaila, Kawin

doi:10.1190/segam2014-0882.1

Cited by 34 publications

(17 citation statements)

References 5 publications

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“…Recently, Rickett et al (2014) and King and Poole (2015) show results in which such a closed-loop deghosting process is applied in the local plane-wave domain and can adapt to small errors in the propagation operator. In Wang et al (2014), a similar method is combined with interpolation in the crossline direction to make it applicable to 3D data with coarse sampling in one direction. Berkhout and Blacquière (2016) show a closed-loop approach based on a nonlinear deblending algorithm.…”

Section: Discussionmentioning

confidence: 99%

Integrated receiver deghosting and closed-loop surface-multiple elimination

2017

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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.

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Section: Discussionmentioning

confidence: 99%

Integrated receiver deghosting and closed-loop surface-multiple elimination

2017

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“…The reweighting technique can be used in the second run to further reduce possible aliasing (Wang et al . ) or in the first run to improve the resolution. To reduce the risk of missing weak events, this process can be repeated several times called iterations.…”

Section: Explicit Three‐dimensional Deghosting Algorithmmentioning

confidence: 99%

“…The sparseness constraint reduces the number of unknowns and mitigates the aliasing problem (Wang et al . ). We refer to this method as the explicit 3D method as it explicitly accounts for the crossline slowness.…”

Section: Introductionmentioning

confidence: 97%

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Evolution of deghosting process for single‐sensor streamer data from 2D to 3D

Zhang¹,

Masoomzadeh

Wang³

2018

Geophysical Prospecting

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In marine acquisition, reflections of sound energy from the water–air interface result in ghosts in the seismic data, both in the source side and the receiver side. Ghosts limit the bandwidth of the useful signal and blur the final image. The process to separate the ghost and primary signals, called the deghosting process, can fill the ghost notch, broaden the frequency band, and help achieve high‐resolution images. Low‐signal‐to‐noise ratio near the notch frequencies and 3D effects are two challenges that the deghosting process has to face. In this paper, starting from an introduction to the deghosting process, we present and compare two strategies to solve the latter. The first is an adaptive mechanism that adjusts the deghosting operator to compensate for 3D effects or errors in source/receiver depth measurement. This method does not include explicitly the crossline slowness component and is not affected by the sparse sampling in the same direction. The second method is an inversion‐type approach that does include the crossline slowness component in the algorithm and handles the 3D effects explicitly. Both synthetic and field data examples in wide azimuth acquisition settings are shown to compare the two strategies. Both methods provide satisfactory results.

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“…Wu et al . () and Wang, Ray and Nimsaila () used a progressive sparse τ−p x ‐p y inversion method to perform 3D joint deghosting and crossline interpolation using single‐component pressure data. Kragh et al .…”

Section: Introductionmentioning

confidence: 99%

Three‐dimensional receiver deghosting of seismic streamer data using L1 inversion and redundant extended radon dictionary

Sun¹,

Verschuur

2018

Geophysical Prospecting

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In this paper, we propose a novel three‐dimensional receiver deghosting algorithm that is capable of deghosting both horizontal and slanted streamer data in a theoretically consistent manner. Our algorithm honours wave propagation phenomena in a true three‐dimensional sense and frames the three‐dimensional receiver deghosting problem as a Lasso problem. The ultimate goal is to minimise the mismatch between the actual measurements and the simulated wavefield with an L1 constraint applied in the extended Radon space to handle the underdetermined nature of this problem. We successfully demonstrate our algorithm on a modified three‐dimensional EAGE/SEG Overthrust model and a Red Sea marine dataset.

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3D joint deghosting and crossline interpolation for marine single-component streamer data

Cited by 34 publications

References 5 publications

Integrated receiver deghosting and closed-loop surface-multiple elimination

Integrated receiver deghosting and closed-loop surface-multiple elimination

Evolution of deghosting process for single‐sensor streamer data from 2D to 3D

Three‐dimensional receiver deghosting of seismic streamer data using L1 inversion and redundant extended radon dictionary

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