Broadband Processing of Conventional Streamer Data - Optimized De-Ghosting in the Tau-P Domain

Masoomzadeh, Hassan; Woodburn, N.

doi:10.3997/2214-4609.20130093

Cited by 8 publications

(8 citation statements)

References 5 publications

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“…During the past decade, this topic has attracted more attention as more and more computation power becomes available, also being driven by the ever‐increasing requirement for high‐resolution images from the industry (Zhou et al . ; Masoomzadeh and Woodburn ; Telling et al . ).…”

Section: Introductionmentioning

confidence: 99%

“…Since linear Radon transformation is a mature technology and has been used by the industry for many years, this method is a popular choice (Zhou et al . ; Masoomzadeh and Woodburn ; Telling et al . ; Zhang et al .…”

Section: Introductionmentioning

confidence: 99%

“…However, applications in production were quite limited until recent years when significant progress has been made in both acquisition and processing methods. During the past decade, this topic has attracted more attention as more and more computation power becomes available, also being driven by the ever-increasing requirement for high-resolution images from the industry (Zhou et al 2012;Masoomzadeh and Woodburn 2013;Telling et al 2014). The processing-based methods mostly work in the frequency domain and apply plane-wave decomposition directly or indirectly, and these methods are the main topic of this paper.…”

Section: Introductionmentioning

confidence: 99%

“…The deghosted data in the frequency-slowness domain are then transformed back into the time-offset (t-x-y) domain. Since linear Radon transformation is a mature technology and has been used by the industry for many years, this method is a popular choice (Zhou et al 2012;Masoomzadeh and Woodburn 2013;Telling et al 2014;Zhang et al 2015). This set of algorithms works for streamers towed at a constant depth.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

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“…The deghosting filter is commonly represented in the frequency-wavenumber ðω-k x -k y Þ domain or, alternatively in its equivalent, the intercept time-slowness ðτ-p x -p y Þ domain. Such methods are sensitive to the S/N, but they can often provide effective deghosting under certain controlled conditions, such as assumptions about the sea surface and the properties of the water column (Masoomzadeh and Woodburn, 2013;Wang et al, 2016). There are also various schemes aiming at deghosting for variable-depth streamer data being developed in recent years (Soubaras, 2012;Poole, 2013;Robertsson and Amundsen, 2014;Grion et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A low-frequency deghosting method: Analysis and numerical tests

2017

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The low-frequency component of seismic data can be beneficial for several reasons: improved signal penetration into the earth, enhanced resolution, and better constrained inversion results. We have developed a detailed analysis of a deghosting solution for the low-frequency spectrum of marine seismic pressure data. The advantages of this low-frequency deghosting method are: (1) it can be applied in the spatial domain, (2) it is applicable for horizontal streamers and for streamers with a mild depth variation, and (3) it is a fast-track solution that can be used flexibly as a preprocessing, or premigration step. The disadvantages of this method are: (1) it is an approximation to the full-deghosting operator and cannot infill the ghost notches of the spectrum, except near 0 Hz, and (2) it has decreasing effectiveness with a larger source/receiver depth. Numerical tests on the synthetic and field data sets indicate that this method is promising in deghosting data, up to at least half the frequency of the first nonzero ghost notch.

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