Sea Surface Reflection Coefficient Estimation

Orji, Okwudili C.; Söllner, Walter; Gelius, Leiv‐J.

doi:10.1190/segam2013-0944.1

Cited by 28 publications

(11 citation statements)

References 16 publications

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“…Both numerical simulation and theoretical analysis have been utilised to find a better estimate of the reflection coefficient in terms of the wave height and incident angle (Jovanovich et al . ; Orji, Sollner and Gelius ). In this paper, we ignore the effect of the incident angle and choose

r (f)

r false(f false) = r_{0} e^{- \frac{1}{σ^{2}} f^{2}},

where σ is a positive parameter that determines how fast the reflection coefficient decreases with frequency and r 0 is the reflection coefficient at 0 Hz.…”

Section: Ghosting and Deghosting Operators Two‐dimensional Deghostinmentioning

confidence: 95%

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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r (f)

r false(f false) = r_{0} e^{- \frac{1}{σ^{2}} f^{2}},

where σ is a positive parameter that determines how fast the reflection coefficient decreases with frequency and r 0 is the reflection coefficient at 0 Hz.…”

Section: Ghosting and Deghosting Operators Two‐dimensional Deghostinmentioning

confidence: 95%

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

Zhang¹,

Masoomzadeh

Wang³

2018

Geophysical Prospecting

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show abstract

“…This approach will probably result in more accurate near offsets and surface multiples. In a rough and or varying sea, R is not equal to −I, especially for higher frequencies (Orji et al, 2013). In practice, a wrong R will result in ringing events and to some extent, due to the L1-norm, the algorithm still would be able to suppress these effects.…”

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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“…Although in most reported methods R is always assumed to be

- I

, in reality, its behaviour is much more complex as the water surface cannot be perfectly flat. To our knowledge, this point has not been dwelled on adequately yet (Orji, Sollner and Gelius ); however, we leave 3D receiver deghosting with variable R for future research, and for the time being, R is assumed to be

- I

.…”

Section: Theory and Algorithmmentioning

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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Sea Surface Reflection Coefficient Estimation

Cited by 28 publications

References 16 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

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

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