| Building realistic structure models to train convolutional neural networks for seismic structural interpretation X Wu, Z Geng, Y Shi, N Pham, S Fomel, G Caumon Geophysics 85 (4), WA27-WA39, 2020 | 176 | 2020 |
| Automatic channel detection using deep learning N Pham, S Fomel, D Dunlap Interpretation 7 (3), SE43-SE50, 2019 | 135 | 2019 |
| Seismic data interpolation using CycleGAN H Kaur, N Pham, S Fomel SEG technical program expanded abstracts 2019, 2202-2206, 2019 | 76 | 2019 |
| Missing well log prediction using convolutional long short-term memory network N Pham, X Wu, E Zabihi Naeini Geophysics 85 (4), WA159-WA171, 2020 | 75 | 2020 |
| Seismic data interpolation using deep learning with generative adversarial networks H Kaur, N Pham, S Fomel Geophysical Prospecting 69 (2), 307-326, 2021 | 70 | 2021 |
| Improving resolution of migrated images by approximating the inverse Hessian using deep learning H Kaur, N Pham, S Fomel Geophysics 85 (4), WA173–WA183, 2020 | 64 | 2020 |
| Seismic ground‐roll noise attenuation using deep learning H Kaur, S Fomel, N Pham Geophysical Prospecting 68 (7), 2064-2077, 2020 | 60 | 2020 |
| Scalodeep: A highly generalized deep learning framework for real‐time earthquake detection OM Saad, G Huang, Y Chen, A Savvaidis, S Fomel, N Pham, Y Chen Journal of Geophysical Research: Solid Earth 126 (4), e2020JB021473, 2021 | 52 | 2021 |
| Physics-constrained deep learning for ground roll attenuation N Pham, W Li Geophysics 87 (1), V15-V27, 2022 | 28 | 2022 |
| Overcoming numerical dispersion of finite-difference wave extrapolation using deep learning H Kaur, S Fomel, N Pham SEG International Exposition and Annual Meeting, D033S076R002, 2019 | 28 | 2019 |
| Elastic wave-mode separation in heterogeneous anisotropic media using deep learning H Kaur, S Fomel, N Pham Seg technical program expanded abstracts 2019, 2654-2658, 2019 | 20 | 2019 |
| Missing well log prediction using deep recurrent neural networks N Pham, EZ Naeini 81st EAGE Conference and Exhibition 2019 2019 (1), 1-5, 2019 | 16 | 2019 |
| High-fidelity permeability and porosity prediction using deep learning with the self-attention mechanism L Yang, S Wang, X Chen, W Chen, OM Saad, X Zhou, N Pham, Z Geng, ... IEEE Transactions on Neural Networks and Learning Systems, 2022 | 15 | 2022 |
| Ground roll attenuation using generative adversarial network H Kaur, S Fomel, N Pham 81st EAGE Conference and Exhibition 2019 2019 (1), 1-5, 2019 | 15 | 2019 |
| Uncertainty estimation using Bayesian convolutional neural network for automatic channel detection N Pham, S Fomel SEG International Exposition and Annual Meeting, D031S068R001, 2020 | 14 | 2020 |
| A deep learning framework for seismic facies classification H Kaur, N Pham, S Fomel, Z Geng, L Decker, B Gremillion, M Jervis, ... Interpretation 11 (1), T107-T116, 2023 | 13 | 2023 |
| A fast algorithm for elastic wave‐mode separation using deep learning with generative adversarial networks (GANS) H Kaur, S Fomel, N Pham Journal of Geophysical Research: Solid Earth 126 (9), e2020JB021123, 2021 | 13 | 2021 |
| Uncertainty and interpretability analysis of encoder-decoder architecture for channel detection N Pham, S Fomel Geophysics 86 (4), O49-O58, 2021 | 13 | 2021 |
| Separating primaries and multiples using hyperbolic Radon transform with deep learning H Kaur, N Pham, S Fomel SEG Technical Program Expanded Abstracts 2020, 1496-1500, 2020 | 13 | 2020 |
| Seismic data interpolation using CycleGAN: 89th Annual International Meeting, SEG, Expanded Abstracts, 2202–2206, doi: 10.1190/segam2019-3207424.1 H Kaur, N Pham, S Fomel Abstract, 2019 | 13 | 2019 |
Sergey FomelProfessor of Geophysics, Jackson School of Geosciences, University of Texas at AustinZweryfikowany adres z beg.utexas.edu
