| The importance of transfer learning in seismic modeling and imaging A Siahkoohi, M Louboutin, FJ Herrmann Geophysics 84 (6), A47-A52, 2019 | 67 | 2019 |
| Seismic data reconstruction with generative adversarial networks A Siahkoohi, R Kumar, F Herrmann 80th EAGE conference and exhibition 2018 2018 (1), 1-5, 2018 | 65 | 2018 |
| Surface-related multiple elimination with deep learning A Siahkoohi, DJ Verschuur, FJ Herrmann SEG International Exposition and Annual Meeting, 2019 | 44* | 2019 |
| Learned imaging with constraints and uncertainty quantification FJ Herrmann, A Siahkoohi, G Rizzuti NeurIPS 2019 Deep Inverse Workshop, 2019 | 29* | 2019 |
| Preconditioned training of normalizing flows for variational inference in inverse problems A Siahkoohi, G Rizzuti, M Louboutin, PA Witte, FJ Herrmann 3rd Symposium on Advances in Approximate Bayesian Inference, 2021 | 26* | 2021 |
| 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 | 18* | 2020 |
| 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 | 18* | 2020 |
| Deep Bayesian inference for seismic imaging with tasks A Siahkoohi, G Rizzuti, FJ Herrmann Geophysics 87 (5), S281-S302, 2022 | 17* | 2022 |
| Faster uncertainty quantification for inverse problems with conditional normalizing flows A Siahkoohi, G Rizzuti, PA Witte, FJ Herrmann arXiv preprint arXiv:2007.07985, 2020 | 15* | 2020 |
| Deep-learning based ocean bottom seismic wavefield recovery A Siahkoohi, R Kumar, FJ Herrmann SEG International Exposition and Annual Meeting, 2019 | 15* | 2019 |
| Learned iterative solvers for the Helmholtz equation G Rizzuti, A Siahkoohi, FJ Herrmann 81st EAGE Conference and Exhibition 2019 2019 (1), 1-5, 2019 | 15 | 2019 |
| 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 | 14* | 2020 |
| Neural network augmented wave-equation simulation A Siahkoohi, M Louboutin, FJ Herrmann arXiv preprint arXiv:1910.00925, 2019 | 13* | 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 | 13 | 2018 |
| Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators Z Yin, A Siahkoohi, M Louboutin, FJ Herrmann Second International Meeting for Applied Geoscience & Energy, 467-472, 2022 | 12 | 2022 |
| Reliable amortized variational inference with physics-based latent distribution correction A Siahkoohi, G Rizzuti, R Orozco, FJ Herrmann Geophysics 88 (3), R297-R322, 2023 | 10 | 2023 |
| 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 | 10 | 2021 |
| InvertibleNetworks.jl: A Julia framework for invertible neural networks P Witte, G Rizzuti, M Louboutin, A Siahkoohi, F Herrmann https://zenodo.org/record/7430615, 2020 | 7* | 2020 |
| Weak deep priors for seismic imaging A Siahkoohi, G Rizzuti, FJ Herrmann SEG Technical Program Expanded Abstracts 2020, 2998-3002, 2020 | 6 | 2020 |
| Self-consuming generative models go MAD S Alemohammad, J Casco-Rodriguez, L Luzi, AI Humayun, H Babaei, ... arXiv preprint arXiv:2307.01850, 2023 | 5 | 2023 |
Felix J. HerrmannProfessor Schools of Earth and Atmospheric Sciences, Computational Science and EngineeringVerified email at gatech.edu
