Slanted-streamer Data-adaptive Deghosting with Local Plane Waves

Rickett, James; Manen, D.J. van; Loganathan, P.; Seymour, Nigel

doi:10.3997/2214-4609.20141453

Cited by 41 publications

(10 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the L1 norm or L2 norm of the deghosted data can be used as a criterion in searching (Rickett et al . ). Kurtosis is another reported functional statistical measure (Grion, Telling and Barnes ), defined as

k false(x false) = \frac{E [{(x - η)}^{4}]}{{false(E [{(x - η)}^{2}] false)}^{2}},

where η is the mean of x .…”

Section: Implicit Three‐dimensional Algorithmmentioning

confidence: 97%

See 1 more Smart Citation

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

Zhang¹,

Masoomzadeh

Wang³

2018

Geophysical Prospecting

View full text Add to dashboard Cite

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.

show abstract

k false(x false) = \frac{E [{(x - η)}^{4}]}{{false(E [{(x - η)}^{2}] false)}^{2}},

where η is the mean of x .…”

Section: Implicit Three‐dimensional Algorithmmentioning

confidence: 97%

“…This algorithm is both time and space variant (Rickett et al . ; Zhang et al . ) and avoids the difficulty of calculating the crossline slowness component.…”

Section: Implicit Three‐dimensional Algorithmmentioning

confidence: 99%

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

Zhang¹,

Masoomzadeh

Wang³

2018

Geophysical Prospecting

View full text Add to dashboard Cite

show abstract

“…A dedicated deghosting algorithm can be implemented in a closed-loop manner without the surface-multiple prediction. 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.…”

Section: Discussionmentioning

confidence: 99%

“…In practice, uncertainties in the estimated water velocity, receiver depth, and a rough sea can lead to errors in the ghost model. To handle these uncertainties, Rickett et al (2014) and King and Poole (2015) propose adaptive deghosting algorithms that take into account small deviations in these parameters. Grion et al (2015) describe a method to maximize the kurtosis of the autocorrelation function, to determine which parameters give the best deghosting result.…”

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

“…The term 'slanted' here means variable-depth streamers with strictly linear shape. Rickett et al (2014) have presented an algorithm for flat and slanted streamer acquisition. It is based on local plane-wave decomposition, and the upgoing wavefield is estimated together with the ghost delay in the local plane-wave domain.…”

Section: Introductionmentioning

confidence: 99%

A method for deghosting of data recorded with a streamer of arbitrary shape in rough sea conditions

Denisov

Egorov

Burtsev

2018

Geophysical Prospecting

View full text Add to dashboard Cite

Deghosting of pressure‐only data has become a routine in marine seismic processing. Most existing techniques suffer from noise susceptibility or excessive simplification of the used ghost model, which leads to difficulties in removing the ghost waves. The algorithm presented in this paper is based on the wavefield extrapolation theory, and is capable of taking into account arbitrary streamer shapes and rough sea surfaces. The computations are performed in the frequency domain and come down to solving systems of linear equations. Regularization and data‐adaptive statistical optimization of the parameters prevent noise amplification. We describe the theory of the method and test it against synthetic and field datasets with different streamer shapes for both rough and flat sea surfaces.

show abstract

Slanted-streamer Data-adaptive Deghosting with Local Plane Waves

Cited by 41 publications

References 0 publications

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

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

Integrated receiver deghosting and closed-loop surface-multiple elimination

A method for deghosting of data recorded with a streamer of arbitrary shape in rough sea conditions

Contact Info

Product

Resources

About