Capturing the Value of Source-Over-Streamer Acquisition for Velocity Model Building at Barents Sea

Salaun, N.; Reinier, M.; Henin, G.; Gigou, G.; Wright, Andrew

doi:10.3997/2214-4609.202011945

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…We tested the performance of the convolutional encoder–decoder (CED)‐based interpolation and regularization of 3D offset classes (OFCs) on a 3D synthetic data set and a real data set. Real data from a marine wide‐towed penta‐source‐over‐streamer (Poole et al ., 2020; Salaun et al ., 2020) survey are used for demonstration purposes. Even though this source‐over‐streamer setup enabled the acquisition of short offsets, the ultra‐near OFCs were still sparsely sampled.…”

Section: Synthetic and Real Data Examplesmentioning

confidence: 99%

De‐migration‐based supervised learning for interpolation and regularization of 3D offset classes

Hlebnikov

Greiner

Vinje³

et al. 2022

Geophysical Prospecting

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Regularization and interpolation of 3D offset classes prior to imaging are an important and challenging step in the marine seismic data processing flow. Here we describe how to perform this task using a deep neural network, and we explain how to overcome the challenge of creating a suitable training data set. The training data set is generated by de-migrating stacked pre-stack depth migration images. For each offset class volume, we de-migrate the pre-stack depth migrated stacked image into two configurations: (i) the original survey configuration consisting of the recorded source/receiver positions and (ii) an 'Ideal' survey configuration with constant offset and azimuth for each 3D offset class. The training creates a 3D convolutional encoder-decoder model that will regularize and interpolate seismic data. The convolutional encoderdecoder is trained on 3D sliding windows in each 3D offset cube to map from (i) to (ii), i.e. to map the original survey configuration with irregular and sparse sampling into the fully sampled regular offset cubes suitable for offset-based migration, such as Kirchhoff migration. Such migration algorithms rely on regular and sufficiently dense sampling to achieve constructive interference to image the structures and destructive interference to suppress migration noise. We test the new method on one synthetic and one field data example and show that it performs better than a standard regularization/interpolation method based on anti-leakage Fourier transform, especially for the smallest offset classes. On the synthetic data, we also demonstrate that the convolutional encoder-decoder method preserves the amplitude versus offset as well as the standard method.

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Section: Synthetic and Real Data Examplesmentioning

confidence: 99%

De‐migration‐based supervised learning for interpolation and regularization of 3D offset classes

Hlebnikov

Greiner

Vinje³

et al. 2022

Geophysical Prospecting

View full text Add to dashboard Cite

show abstract

“…OFCs on a 3D synthetic data set and a real data set. Real data from a marine wide-towed penta-source-over-streamer (Poole et al, 2020;Salaun et al, 2020) survey is used for demonstration purposes. Even though this source-over-streamer setup enabled the acquisition of short offsets, the ultra-near OFCs were still sparsely sampled.…”

Section: Synthetic and Real Data Examplesmentioning

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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Capturing the Value of Source-Over-Streamer Acquisition for Velocity Model Building at Barents Sea

Cited by 2 publications

References 2 publications

De‐migration‐based supervised learning for interpolation and regularization of 3D offset classes

De‐migration‐based supervised learning for interpolation and regularization of 3D offset classes

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

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