Proposal of the Dunet Neural Network Architecture: Deghosting Example and Theoretical Analysis

Peng, Houwen; Messud, Jérémie; Salaun, N.; Hammoud, Issa; Jeunesse, P.; Lesieur, Thibault; Lacombe, C.

doi:10.3997/2214-4609.202112820

Cited by 3 publications

(6 citation statements)

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“…Peng et al. (2021) showed that it is possible to create training data directly from data after conventional deghosting (pre‐stack). One issue with this method is that the trained neural network will most likely never improve the deghosting as it will reproduce the weaknesses of conventional deghosting.…”

Section: Discussionmentioning

confidence: 99%

“…A few other papers have also used the U‐net structure for pressure‐only deghosting with high‐quality results (de Jonge, Vinje, Zhao, et al., 2022; Peng et al., 2021; Vrolijk & Blacquière, 2021). In our case, we are confident that a U‐net structure will also give good results on dual‐component data.…”

Section: Methodsmentioning

confidence: 99%

“…While multiples behave similarly to primary reflections, refractions behave differently, and for this reason, our neural network struggled more to deghost them compared to multiples. Peng et al (2021) showed that it is possible to create training data directly from data after conventional deghosting (pre-stack). One issue with this method is that the trained neural network will most likely never improve the deghosting as it will reproduce the weaknesses of conventional deghosting.…”

Section: Real Data -Northern Viking Graben Datasetmentioning

confidence: 99%

“…Machine learning and, more specifically, artificial neural networks have recently been used for various processing steps such as swell noise attenuation (Zhao et al., 2019), debubbling (de Jonge, Vinje, Poole, et al., 2022), seismic inference noise attenuation (Sun et al., 2019; Sun & Hou, 2022) and interpolation (Greiner et al., 2019; Hlebnikov et al., 2022). Several papers have also described the use of neural networks for deghosting (Almuteri & Sava, 2021; de Jonge, Vinje, Zhao, et al., 2022; Peng et al., 2021; Vrolijk & Blacquiere, 2020). However, none of these methods used multi‐component data for deghosting.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Real Data -Northern Viking Graben Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Jonge

Vinje

Poole³

et al. 2023

Geophysical Prospecting

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“…Because seismic data are never noise-free (Paper I), obtaining the labeled (noise-free) data usually are met using conventional noise attenuation methods or recorded noise. We can then use DNN methods to (almost) mimic the conventional approach (Siahkoohi et al, 2019a;Peng et al, 2021). However, these methods can hardly outperform the conventional approach used in the first place.…”

Section: Motivation Background and Scopementioning

confidence: 99%

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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