Deep-convolutional neural networks in prestack seismic: Two exploratory examples

Siahkoohi, Ali; Louboutin, Mathias; Kumar, Rajiv; Herrmann, Felix J.

doi:10.1190/segam2018-2998599.1

Cited by 14 publications

(14 citation statements)

References 15 publications

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“…In this work, which extends our previous attempt to eliminate surface-related multiples from synthetic data (Siahkoohi et al, 2018b), we are merely interested in exploring potential capabilities of CNNs in dealing with the free surface on a field data set. We explore the possibility of approximating the action of the expensive EPSI algorithm with a neural network.…”

Section: Theorymentioning

confidence: 99%

“…The described neural network is used as the generator in the GAN framework. Because its initial success in removing multiples (Siahkoohi et al, 2018b), we use the network described in Isola et al (2017) for the discriminator.…”

Section: Cnn Architecturementioning

confidence: 99%

“…Machine learning is rapidly attracting interest in the exploration seismology research community. In the past few years, there has been numerous attempts to deploy deep learning algorithms to address problems in active research areas in the field of seismic, including but not limited to pre-stack seismic data processing (Mikhailiuk and Faul, 2018;Siahkoohi et al, 2018a;Ovcharenko et al, 2018;Sun and Demanet, 2018;Siahkoohi et al, 2018b), modeling and imaging (Moseley et al, 2018;Siahkoohi et al, 2019;Rizzuti et al, 2019), and inversion (Lewis and Vigh, 2017;Araya-Polo et al, 2018;Richardson, 2018;Das et al, 2018;Kothari et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Surface-related multiple elimination with deep learning

Siahkoohi

Verschuur

Herrmann

2019

SEG Technical Program Expanded Abstracts 2019

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We explore the potential of neural networks in approximating the action of the computationally expensive Estimation of Primaries by Sparse Inversion (EPSI) algorithm, applied to real data, via a supervised learning algorithm. We show that given suitable training data, consisting of a relatively cheap prediction of multiples and pairs of shot records with and without surface-related multiples, obtained via EPSI, a well-trained neural network is capable of providing an approximation to the action of the EPSI algorithm. We perform our numerical experiment on the field Nelson data set. Our results demonstrate that the quality of the multiple elimination via our neural network improves compared to the case where we only feed the network with shot records with surface-related multiples. We take these benefits by supplying the neural network with a relatively poor prediction of the multiples, e.g. obtained by a relatively cheap single step of Surface-Related Multiple Elimination.

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Section: Theorymentioning

confidence: 99%

Section: Cnn Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Surface-related multiple elimination with deep learning

Siahkoohi

Verschuur

Herrmann

2019

SEG Technical Program Expanded Abstracts 2019

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“…The advantage is reduced computational complexity, on the one hand, and simplified training phase, on the other. We are mainly inspired by the recent works of [1], which combines gradient-based optimization and deep learning for inverse problems, and [11], where finite-difference time-domain modeling is trained to reduce numerical dispersion.…”

Section: Introductionmentioning

confidence: 99%

Learned Iterative Solvers for the Helmholtz Equation

Rizzuti¹,

Siahkoohi²,

Herrmann³

2019

81st EAGE Conference and Exhibition 2019

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We propose a 'learned' iterative solver for the Helmholtz equation, by combining traditional Krylov-based solvers with machine learning. The method is, in principle, able to circumvent the shortcomings of classical iterative solvers, and has clear advantages over purely data-driven approaches. We demonstrate the effectiveness of this approach under a 1.5-D assumption, when adequate a priori information about the velocity distribution is known.

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“…Recent successes in the application of machine learning algorithms have been reported in the areas of seismic interpretation (Huang et al, 2017;Di et al, 2018;Veillard et al, 2018), seismic data processing (Siahkoohi et al, 2018b;Sun and Demanet, 2018;Ovcharenko et al, 2018), seismic modeling and imaging Rizzuti et al, 2019;Moseley et al, 2018), and inversion (Mosser et al, 2018;Kothari et al, 2019). Several authors have addressed the seismic wavefield recovery (Siahkoohi et al, 2018a;Mandelli et al, 2018) as well.…”

Section: Introductionmentioning

confidence: 99%

Deep-learning based ocean bottom seismic wavefield recovery

Siahkoohi

Kumar

Herrmann

2019

SEG Technical Program Expanded Abstracts 2019

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Ocean bottom surveys usually suffer from having very sparse receivers. Assuming a desirable source sampling, achievable by existing methods such as (simultaneous-source) randomized marine acquisition, we propose a deep-learning based scheme to bring the receivers to the same spatial grid as sources using a convolutional neural network. By exploiting source-receiver reciprocity, we construct training pairs by artificially subsampling the fully-sampled single-receiver frequency slices using a random training mask and later, we deploy the trained neural network to fill-in the gaps in single-source frequency slices. Our experiments show that a random training mask is essential for successful wavefield recovery, even when receivers are on a periodic gird. No external training data is required and experiments on a 3D synthetic data set demonstrate that we are able to recover receivers for up to 90% missing receivers, missing either randomly or periodically, with a better recovery for random case, at low to midrange frequencies.

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Deep-convolutional neural networks in prestack seismic: Two exploratory examples

Cited by 14 publications

References 15 publications

Surface-related multiple elimination with deep learning

Surface-related multiple elimination with deep learning

Learned Iterative Solvers for the Helmholtz Equation

Deep-learning based ocean bottom seismic wavefield recovery

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