Ali Siahkoohi
Title
Cited by
Cited by
Year
Seismic data reconstruction with generative adversarial networks
A Siahkoohi, R Kumar, F Herrmann
80th EAGE Conference and Exhibition 2018 2018 (1), 1-5, 2018
422018
The importance of transfer learning in seismic modeling and imaging
A Siahkoohi, M Louboutin, FJ Herrmann
Geophysics 84 (6), A47-A52, 2019
242019
Learned imaging with constraints and uncertainty quantification
FJ Herrmann, A Siahkoohi, G Rizzuti
arXiv preprint arXiv:1909.06473, 2019
182019
Surface-related multiple elimination with deep learning
A Siahkoohi, DJ Verschuur, FJ Herrmann
SEG International Exposition and Annual Meeting, 2019
172019
A deep-learning based bayesian approach to seismic imaging and uncertainty quantification
A Siahkoohi, G Rizzuti, F Herrmann
82nd EAGE Annual Conference & Exhibition 2020 (1), 1-5, 2020
112020
Learned iterative solvers for the Helmholtz equation
G Rizzuti, A Siahkoohi, FJ Herrmann
81st EAGE Conference and Exhibition 2019 2019 (1), 1-5, 2019
112019
Deep-learning based ocean bottom seismic wavefield recovery
A Siahkoohi, R Kumar, FJ Herrmann
SEG International Exposition and Annual Meeting, 2019
102019
Parameterizing uncertainty by deep invertible networks: An application to reservoir characterization
G Rizzuti, A Siahkoohi, PA Witte, FJ Herrmann
SEG Technical Program Expanded Abstracts 2020, 1541-1545, 2020
92020
Deep-convolutional neural networks in prestack seismic—two exploratory examples
A Siahkoohi, M Louboutin, R Kumar, FJ Herrmann
SEG Technical Program Expanded Abstracts 2018, 2196-2200, 2018
82018
Seismic data reconstruction with generative adversarial networks: 80th Annual International Conference and Exhibition, EAGE
A Siahkoohi, R Kumar, F Herrmann
Extended Abstracts, doi 10 (3997), 2214-4609, 2018
82018
Preconditioned training of normalizing flows for variational inference in inverse problems
A Siahkoohi, G Rizzuti, M Louboutin, PA Witte, FJ Herrmann
arXiv preprint arXiv:2101.03709, 2021
72021
Uncertainty quantification in imaging and automatic horizon tracking—a Bayesian deep-prior based approach
A Siahkoohi, G Rizzuti, FJ Herrmann
SEG Technical Program Expanded Abstracts 2020, 1636-1640, 2020
62020
Faster uncertainty quantification for inverse problems with conditional normalizing flows
A Siahkoohi, G Rizzuti, PA Witte, FJ Herrmann
arXiv preprint arXiv:2007.07985, 2020
62020
Neural network augmented wave-equation simulation
A Siahkoohi, M Louboutin, FJ Herrmann
arXiv preprint arXiv:1910.00925, 2019
52019
Weak deep priors for seismic imaging
A Siahkoohi, G Rizzuti, FJ Herrmann
SEG Technical Program Expanded Abstracts 2020, 2998-3002, 2020
42020
Surface-related multiple elimination with deep learning: 89th Annual International Meeting, SEG, Expanded Abstracts, 4629–4634, doi: 10.1190/segam2019-3216723.1
A Siahkoohi, DJ Verschuur, FJ Herrmann
Abstract, 2019
4*2019
The importance of transfer learning in seismic modeling and imaging: GEOPHYSICS, 84
A Siahkoohi, M Louboutin, FJ Herrmann
A47–A52, 2019
42019
Deep-learning based ocean bottom seismic wavefield recovery: 89th Annual International Meeting, SEG, Expanded Abstracts, 2232–2237, doi: 10.1190/segam2019-3216632.1
A Siahkoohi, R Kumar, F Herrmann
Abstract, 2019
32019
Deep-learning based ocean bottom seismic wavefield recovery: 89th Annual International Meeting, SEG
A Siahkoohi, R Kumar, FJ Herrmann
Expanded Abstracts, doi: https://doi. org/10.1190/segam2019-3216632.1, 2019
32019
Learned imaging with constraints and uncertainty quantification: Presented at the Neural Information Processing Systems (NeurIPS) 2019 Deep Inverse Workshop
FJ Herrmann, A Siahkoohi, G Rizzuti
32019
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