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Ali Siahkoohi
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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
502018
The importance of transfer learning in seismic modeling and imaging
A Siahkoohi, M Louboutin, FJ Herrmann
Geophysics 84 (6), A47-A52, 2019
44*2019
Surface-related multiple elimination with deep learning
A Siahkoohi, DJ Verschuur, FJ Herrmann
SEG International Exposition and Annual Meeting, 2019
35*2019
Learned imaging with constraints and uncertainty quantification
FJ Herrmann, A Siahkoohi, G Rizzuti
arXiv preprint arXiv:1909.06473, 2019
24*2019
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
172020
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
162021
A deep-learning based bayesian approach to seismic imaging and uncertainty quantification
A Siahkoohi, G Rizzuti, F Herrmann
EAGE 2020 Annual Conference & Exhibition Online 2020 (1), 1-5, 2020
14*2020
Learned iterative solvers for the Helmholtz equation
G Rizzuti, A Siahkoohi, FJ Herrmann
81st EAGE Conference and Exhibition 2019 2019 (1), 1-5, 2019
132019
Deep-learning based ocean bottom seismic wavefield recovery
A Siahkoohi, R Kumar, FJ Herrmann
SEG International Exposition and Annual Meeting, 2019
12*2019
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
122018
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
112020
Neural network augmented wave-equation simulation
A Siahkoohi, M Louboutin, FJ Herrmann
arXiv preprint arXiv:1910.00925, 2019
10*2019
Faster uncertainty quantification for inverse problems with conditional normalizing flows
A Siahkoohi, G Rizzuti, PA Witte, FJ Herrmann
arXiv preprint arXiv:2007.07985, 2020
82020
Deep Bayesian inference for seismic imaging with tasks
A Siahkoohi, G Rizzuti, FJ Herrmann
Geophysics 87 (5), S281-S302, 2022
72022
Learning by example: fast reliability-aware seismic imaging with normalizing flows
A Siahkoohi, FJ Herrmann
First International Meeting for Applied Geoscience & Energy, 1580-1585, 2021
72021
Weak deep priors for seismic imaging
A Siahkoohi, G Rizzuti, FJ Herrmann
SEG Technical Program Expanded Abstracts 2020, 2998-3002, 2020
52020
InvertibleNetworks. jl: A Julia framework for invertible neural networks
P Witte, G Rizzuti, M Louboutin, A Siahkoohi, F Herrmann
November, 2020
52020
Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators
Z Yin, A Siahkoohi, M Louboutin, FJ Herrmann
arXiv preprint arXiv:2203.14396, 2022
32022
Photoacoustic imaging with conditional priors from normalizing flows
R Orozco, A Siahkoohi, G Rizzuti, T van Leeuwen, FJ Herrmann
NeurIPS 2021 Workshop on Deep Learning and Inverse Problems, 2021
32021
Wave-equation-based inversion with amortized variational Bayesian inference
A Siahkoohi, R Orozco, G Rizzuti, FJ Herrmann
arXiv preprint arXiv:2203.15881, 2022
22022
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Articles 1–20