| Seismic data interpolation using deep learning with generative adversarial networks H Kaur, N Pham, S Fomel Geophysical Prospecting 69 (2), 307-326, 2021 | 99 | 2021 |
| Seismic data interpolation using CycleGAN H Kaur, N Pham, S Fomel SEG technical program expanded abstracts 2019, 2202-2206, 2019 | 93 | 2019 |
| Seismic ground‐roll noise attenuation using deep learning H Kaur, S Fomel, N Pham Geophysical Prospecting 68 (7), 2064-2077, 2020 | 77 | 2020 |
| Improving the resolution of migrated images by approximating the inverse Hessian using deep learning H Kaur, N Pham, S Fomel Geophysics 85 (4), WA173-WA183, 2020 | 73 | 2020 |
| 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 | 34 | 2019 |
| 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 | 23 | 2023 |
| Time-lapse seismic data inversion for estimating reservoir parameters using deep learning H Kaur, Z Zhong, A Sun, S Fomel Interpretation 10 (1), T167-T179, 2022 | 23 | 2022 |
| 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 | 22 | 2019 |
| 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 | 19 | 2021 |
| 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 | 18 | 2019 |
| Ground roll attenuation using generative adversarial network H Kaur, S Fomel, N Pham 81st EAGE Conference and Exhibition 2019 2019 (1), 1-5, 2019 | 16 | 2019 |
| Deep-learning-based 3D fault detection for carbon capture and storage H Kaur, Q Zhang, P Witte, L Liang, L Wu, S Fomel Geophysics 88 (4), IM101-IM112, 2023 | 15 | 2023 |
| 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 |
| Estimating the inverse Hessian for amplitude correction of migrated images using deep learning H Kaur, N Pham, S Fomel SEG International Exposition and Annual Meeting, D033S039R001, 2019 | 13 | 2019 |
| Improving the resolution of migrated images by approximating the inverse Hessian using deep learning. Geophysics 85 H Kaur, N Pham, S Fomel WA173–WA183, 2020 | 7 | 2020 |
| Deep learning framework for true amplitude imaging: Effect of conditioners and initial models H Kaur, J Sun, M Aharchaou, A Baumstein, S Fomel Geophysical Prospecting 72 (Machine learning applications in geophysical …, 2023 | 5 | 2023 |
| Boundary conditions for acoustic and elastic wave propagation using deep learning H Kaur, S Fomel, N Pham First International Meeting for Applied Geoscience & Energy, 1390-1394, 2021 | 3 | 2021 |
| Method for Generating Initial Models For Least Squares Migration Using Deep Neural Networks H Kaur, J Sun, M Aharchaou US Patent App. 17/247,608, 2021 | 3 | 2021 |
| Wave-equation time migration S Fomel, H Kaur Geophysics 86 (1), S103-S111, 2021 | 3 | 2021 |
| Automated hyperparameter optimization for simulating boundary conditions for acoustic and elastic wave propagation using deep learning H Kaur, S Fomel, N Pham Geophysics 88 (1), WA309-WA318, 2023 | 2 | 2023 |