Over/under acquisition and data processing: the next quantum leap in seismic technology?

Hill, D.; Combee, C.; Bacon, John

doi:10.3997/1365-2397.24.1096.26991

Cited by 46 publications

(4 citation statements)

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“…The classical solution to the notch phenomenon was to tow the source and streamer relatively shallow and use the data below the first notch. Other solutions were proposed, such as deconvolution (Jovanovich et al, 1983), and over-understreamer acquisition (Hill et al, 2006), until Carlson et al (2007) introduced the multicomponent streamer, allowing streamers to be towed deeper, enhancing the low frequencies.…”

Section: Marine Seismic Data Acquisitionmentioning

confidence: 99%

“…Several approaches overcoming this bandwidth limitation are available. An early attempt is based on the use of dual streamers towed at two different depths (Hill et al, 2006). More recently, the variable-depth streamer (Soubaras and Dowle, 2010) and the dual-sensor streamer with pressure and velocity sensors (Carlson et al, 2007;Tenghamn et al, 2007) have been introduced.…”

Section: Swell Noisementioning

confidence: 99%

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Noise types and their attenuation in towed marine seismic: A tutorial

Hlebnikov

Elboth²,

Vinje³

et al. 2021

GEOPHYSICS

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The presence of noise in towed marine seismic data is a long-standing problem. The various types of noise present in marine seismic records are never truly random. Instead, seismic noise is more complex and often challenging to attenuate in seismic data processing. Therefore, we examine a wide range of real data examples contaminated by different types of noise including swell noise, seismic interference noise, strumming noise, passing vessel noise, vertical particle velocity noise, streamer hit and fishing gear noise, snapping shrimp noise, spike-like noise, cross-feed noise and streamer mounted devices noise. The noise examples investigated focus only on data acquired with analogue group-forming. Each noise type is classified based on its origin, coherency and frequency content. We then demonstrate how the noise component can be effectively attenuated through industry standard seismic processing techniques. In this tutorial, we avoid presenting the finest details of either the physics of the different types of noise themselves or the noise attenuation algorithms applied. Rather, we focus on presenting the noise problems themselves and show how well the community is able to address such noise. Our aim is that based on the provided insights, the geophysical community will be able to gain an appreciation of some of the most common types of noise encountered in marine towed seismic, in the hope to inspire more researchers to focus their attention on noise problems with greater potential industry impact.

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Section: Marine Seismic Data Acquisitionmentioning

confidence: 99%

Section: Swell Noisementioning

confidence: 99%

Noise types and their attenuation in towed marine seismic: A tutorial

Hlebnikov

Elboth²,

Vinje³

et al. 2021

GEOPHYSICS

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“…Historically, the ghost problem in seismic acquisition was solved by localizing the sources and receivers at shallow depths, usually between 5 and 9 m, to push the notch above the usable frequency range. More recently, other acquisition methods were proposed, such as slant streamers (Bearnth & Moore, 1989), variable‐depth streamers (Soubaras & Dowle, 2010), over‐under streamers (Hill et al., 2006) and multi‐component streamers (Carlson et al., 2007). Slant and variable‐depth streamers aim to attenuate the receiver ghost using the notch‐diversity along the streamer.…”

Section: Introductionmentioning

confidence: 99%

Deghosting dual‐component streamer data using demigration‐based supervised learning

Jonge

Vinje

Poole³

et al. 2023

Geophysical Prospecting

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Ghost reflections from the free surface distort the source signature and generate notches in the seismic amplitude spectrum. For this reason, removing ghost reflections is essential to improve the bandwidth and signal‐to‐noise ratio of seismic data. We have developed a novel approach that involves training a convolutional neural network to remove source and receiver ghosts from marine dual‐component data. High‐quality training data is essential for the network to produce accurate predictions on real data. We have used the demigration of a stacked depth‐migrated image to create training shot gathers. Demigrated pressure and vertical velocity data are used to train the network. We apply the trained network on real pressure and vertical velocity data with ghosts. The network's output may be either source deghosting and receiver deghosting, or both. We test our method on synthetic Marmousi and real North Sea data with dual‐component streamers. The method is compared with conventional dual‐component deghosting using the summation of pressure and vertical velocity. Results show that the method can accurately remove the ghosts with only minor errors in synthetic data. Based on a decimation test, the method is less affected by spatially aliased data than a conventional method, which could benefit data with high frequencies and/or large receiver or cable separations. On real data, the results show consistency with conventional deghosting, both within and outside the training area. This indicates that the method is a viable alternative to conventional methods on real data.

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“…Compensating for the ghost effect has been the subject of geophysical research for many years and two successful solutions have been developed on the receiver side. These are over/under acquisition, where streamers are towed as vertically aligned pairs (Hill et al, 2006) and the use of additional measurements in the streamer, where pressure and particle velocity measurements are combined to achieve the deghosting step (Long et al, 2008). The over/under method requires twice as many streamers to cover the same spread aperture, with a corresponding decrease in acquisition efficiency.…”

Section: Introductionmentioning

confidence: 99%