A 3D Deghosting Solution for Pressure-only Measurements of Conventional Towed Streamer Data

Wang, Y.; Ramírez, Adriana Citlali; Nag, Sayan

doi:10.3997/2214-4609.201601218

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…For example, the linear Radon transformation can be employed to analyse the recorded data in the time domain, followed by the direct application of the deghosting operator in the frequency-slowness domain before applying the inverse linear Radon transform (Masoomzadeh & Woodburn, 2013;Robertsson & Amundsen, 2014;Zhang et al, 2015Zhang et al, , 2018. Similarly, the deghosting operator can also be applied in the frequency-wavenumber (f-k) domain, where, for a given frequency component, each plane-wave is mapped to a specific coefficient in f-k space (Amundsen, 1993;Robinson & Treitel, 2008;Wang et al, 2016). In fact, the ghost time delay does not depend only on the incident angle but also on the streamer's depth.…”

Section: Introductionmentioning

confidence: 99%

Low‐frequency seismic deghosting in a compressed domain using parabolic dictionary learning

Zizi,

Turquais,

Day

et al. 2024

Geophysical Prospecting

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Deghosting is an important technique in the marine seismic industry, as it plays a crucial role in mitigating the effects of ghost reflections from the sea surface, which can significantly impact the accuracy and resolution of subsurface imaging. In recent years, various acquisition‐based techniques have been developed to tackle the challenge of removing receiver–ghost reflections, which is the focus of our paper. These state‐of‐the‐art approaches, such as dual‐sensor or multicomponent towed streamer acquisitions, have demonstrated exceptional accuracy by combining pressure and particle motion data. However, such methods face limitations when dealing with low frequencies due to heavy noise contamination in the particle motion data. Consequently, ghost‐free data reconstruction at low frequencies typically relies on processing‐based approaches, which exclusively utilize recorded pressure data. This study presents a novel deghosting method for low‐frequency applications based on parabolic dictionary learning, which relies solely on recorded pressure data. The proposed method has the advantage of being applicable directly in a compressed domain, eliminating the need for data decompression prior to the deghosting process when compression is applied before the processing steps. This not only reduces costs related to data storage and transfer but also provides a cost‐effective alternative to conventional deghosting by operating directly on the compressed data format, which is smaller in size. The effectiveness of the proposed method was evaluated using both synthetic and field datasets. The results obtained from a synthetic data example indicate that the proposed method achieves similar results to an industry‐standard frequency–wavenumber method, while achieving a compression rate of over 7. Furthermore, the method was tested using a field dataset consisting of a full sail‐line of marine seismic acquisition. The comparison of 2D pre‐stack migrated images between the proposed method and the industry‐standard frequency–wavenumber method revealed insignificant differences, while achieving a compression ratio higher than 5 when our method was used.

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Section: Introductionmentioning

confidence: 99%

Low‐frequency seismic deghosting in a compressed domain using parabolic dictionary learning

Zizi,

Turquais,

Day

et al. 2024

Geophysical Prospecting

View full text Add to dashboard Cite

show abstract

“…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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Broadband seismic over/under sources and their designature-deghosting

et al. 2017

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The source depth influences the frequency band of seismic data. Due to the source ghost effect, it is advantageous to deploy sources deep to enhance the low-frequency content of seismic data. But, for a given source volume, the bubble period decreases with the source depth, thereby degrading the low-frequency content. At the same time, deep sources reduce the seismic bandwidth. Deploying sources at shallower depths has the opposite effects. A shallow source provides improved high-frequency content at the cost of degraded low-frequency content due to the ghosting effect, whereas the bubble period increases with a lesser source depth, thereby slightly improving the low-frequency content. A solution to the challenge of extending the bandwidth on the low- and high-frequency side is to deploy over/under sources, in which sources are towed at two depths. We have developed a mathematical ghost model for over/under point sources fired in sequential and simultaneous modes, and we have found an inverse model, which on common receiver gathers can jointly perform designature and deghosting of the over/under source measurements. We relate the model for simultaneous mode shooting to recent work on general multidepth level array sources, with previous known solutions. Two numerical examples related to over/under sequential shooting develop the main principles and the viability of the method.

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A 3D Deghosting Solution for Pressure-only Measurements of Conventional Towed Streamer Data

Cited by 3 publications

References 1 publication

Low‐frequency seismic deghosting in a compressed domain using parabolic dictionary learning

Low‐frequency seismic deghosting in a compressed domain using parabolic dictionary learning

A low-frequency deghosting method: Analysis and numerical tests

Broadband seismic over/under sources and their designature-deghosting

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