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Proceedings Papers

3D seismic survey design by maximizing the spectral gap

Yijun Zhang, Ziyi Yin, Oscar López, Ali Siahkoohi, Mathias Louboutin, Felix J. Herrmann
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3895546
https://doi.org/10.1190/image2023-3895546.1
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Proceedings Papers

Velocity continuation with Fourier neural operators for accelerated uncertainty quantification

Ali Siahkoohi, Mathias Louboutin, Felix J. Herrmann
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3750475
https://doi.org/10.1190/image2022-3750475.1
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View Paper titled, Velocity continuation with Fourier neural operators for accelerated uncertainty quantification
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Proceedings Papers

Accelerating innovation with software abstractions for scalable computational geophysics

Mathias Louboutin, Philipp Witte, Ali Siahkoohi, Gabrio Rizzuti, Ziyi Yin, Rafael Orozco, Felix J. Herrmann
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3750561
https://doi.org/10.1190/image2022-3750561.1
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Proceedings Papers

A simulation-free seismic survey design by maximizing the spectral gap

Yijun Zhang, Mathias Louboutin, Ali Siahkoohi, Ziyi Yin, Rajiv Kumar, Felix J. Herrmann
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3751690
https://doi.org/10.1190/image2022-3751690.1
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View Paper titled, A simulation-free seismic survey design by maximizing the spectral gap
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Proceedings Papers

Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators

Ziyi Yin, Ali Siahkoohi, Mathias Louboutin, Felix J. Herrmann
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3722848
https://doi.org/10.1190/image2022-3722848.1
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View Paper titled, Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators
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Proceedings Papers

Learning by example: Fast reliability-aware seismic imaging with normalizing flows

Ali Siahkoohi, Felix J. Herrmann
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3581836
https://doi.org/10.1190/segam2021-3581836.1
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View Paper titled, Learning by example: Fast reliability-aware seismic imaging with normalizing flows
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Proceedings Papers

Enabling uncertainty quantification for seismic data preprocessing using normalizing flows (NF) — An interpolation example

Rajiv Kumar, Maria Kotsi, Ali Siahkoohi, Alison Malcolm
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583705
https://doi.org/10.1190/segam2021-3583705.1
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View Paper titled, Enabling uncertainty quantification for seismic data preprocessing using normalizing flows (NF) — An interpolation example
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Proceedings Papers

Parameterizing uncertainty by deep invertible networks: An application to reservoir characterization

Gabrio Rizzuti, Ali Siahkoohi, Philipp A. Witte, Felix J. Herrmann
Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3428150
https://doi.org/10.1190/segam2020-3428150.1
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View Paper titled, Parameterizing uncertainty by deep invertible networks: An application to reservoir characterization
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Proceedings Papers

Uncertainty quantification in imaging and automatic horizon tracking — A Bayesian deep-prior based approach

Ali Siahkoohi, Gabrio Rizzuti, Felix J. Herrmann
Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3417560
https://doi.org/10.1190/segam2020-3417560.1
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View Paper titled, Uncertainty quantification in imaging and automatic horizon tracking — A Bayesian deep-prior based approach
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Proceedings Papers

Weak deep priors for seismic imaging

Ali Siahkoohi, Gabrio Rizzuti, Felix J. Herrmann
Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3417568
https://doi.org/10.1190/segam2020-3417568.1
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View Paper titled, Weak deep priors for seismic imaging
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Proceedings Papers

Transfer learning in large-scale ocean bottom seismic wavefield reconstruction

Mi Zhang, Ali Siahkoohi, Felix J. Herrmann
Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3427882
https://doi.org/10.1190/segam2020-3427882.1
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View Paper titled, Transfer learning in large-scale ocean bottom seismic wavefield reconstruction
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Proceedings Papers

Deep-learning based ocean bottom seismic wavefield recovery

Ali Siahkoohi, Rajiv Kumar, Felix J. Herrmann
Paper presented at the SEG International Exposition and Annual Meeting, September 15–20, 2019
Paper Number: SEG-2019-3216632
https://doi.org/10.1190/segam2019-3216632.1
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View Paper titled, Deep-learning based ocean bottom seismic wavefield recovery
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Proceedings Papers

Surface-related multiple elimination with deep learning

Ali Siahkoohi, Dirk J. Verschuur, Felix J. Herrmann
Paper presented at the SEG International Exposition and Annual Meeting, September 15–20, 2019
Paper Number: SEG-2019-3216723
https://doi.org/10.1190/segam2019-3216723.1
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View Paper titled, Surface-related multiple elimination with deep learning
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Proceedings Papers

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

Ali Siahkoohi, Mathias Louboutin, Rajiv Kumar, Felix J. Herrmann
Paper presented at the 2018 SEG International Exposition and Annual Meeting, October 14–19, 2018
Paper Number: SEG-2018-2998599
https://doi.org/10.1190/segam2018-2998599.1
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View Paper titled, Deep-convolutional neural networks in prestack seismic: Two exploratory examples
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