Cross-streamer wavefield interpolation using deep convolutional neural network

Greiner, Thomas Larsen; Kolbjørnsen, Odd; Lie, J.E.; Nilsen, Espen Harris; Evensen, Andreas Kjelsrud; Gelius, Leiv‐J.

doi:10.1190/segam2019-3214009.1

Cited by 10 publications

(3 citation statements)

References 21 publications

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“…© 2022 Society of Exploration Geophysicists large kernels, typically in the first layer(s), has also been reported in both singleimage super-resolution (Dong et al, 2015) and in super-resolution application within in seismic reconstruction (Greiner et al, 2019). For downsampling of features we employed 3D strides of size k × k × k, which implies a downsampling ratio of k 3 .…”

Section: Convolutional Autoencoder For Multidimensional Reconstructionmentioning

confidence: 99%

Unsupervised deep learning with higher-order total-variation regularization for multidimensional seismic data reconstruction

et al. 2021

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In 3D marine seismic acquisition, the seismic wavefield is not sampled uniformly in the spatial directions. This leads to a seismic wavefield consisting of irregularly and sparsely populated traces with large gaps between consecutive sail-lines especially in the near-offsets. The problem of reconstructing the complete seismic wavefield from a subsampled and incomplete wavefield, is formulated as an underdetermined inverse problem. We investigate unsupervised deep learning based on a convolutional neural network (CNN) for multidimensional wavefield reconstruction of irregularly populated traces defined on a regular grid. The proposed network is based on an encoder-decoder architecture with an overcomplete latent representation, including appropriate regularization penalties to stabilize the solution. We proposed a combination of penalties, which consists of the L2-norm penalty on the network parameters, and a first- and second-order total-variation (TV) penalty on the model. We demonstrate the performance of the proposed method on broad-band synthetic data, and field data represented by constant-offset gathers from a source-over-cable data set from the Barents Sea. In the field data example we compare the results to a full production flow from a contractor company, which is based on a 5D Fourier interpolation approach. In this example, our approach displays improved reconstruction of the wavefield with less noise in the sparse near-offsets compared to the industry approach, which leads to improved structural definition of the near offsets in the migrated sections.

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Section: Convolutional Autoencoder For Multidimensional Reconstructionmentioning

confidence: 99%

Unsupervised deep learning with higher-order total-variation regularization for multidimensional seismic data reconstruction

et al. 2021

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“…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).…”

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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“…Our method is only limited by the available bandwidth in the domain from which we train the upsampling function. In addition, we hypothesize that the nonlinear relationships between the subsampled wavefields and their corresponding target wavefields can be learned along one directionwhere the spacing between sensors is adequately denseand then we use the trained function to reconstruct the wavefield in a coarser direction (Greiner et al, 2019). In the synthetic data example, we use offset gathers in the inline direction representing the dense cases and we attempt to reconstruct the much coarser crossline direction.…”

Section: Introductionmentioning

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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Cross-streamer wavefield interpolation using deep convolutional neural network

Cited by 10 publications

References 21 publications

Unsupervised deep learning with higher-order total-variation regularization for multidimensional seismic data reconstruction

Unsupervised deep learning with higher-order total-variation regularization for multidimensional seismic data reconstruction

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

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

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