| 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 | 227 | 2020 |
| Rstt: Real-time spatial temporal transformer for space-time video super-resolution Z Geng, L Liang, T Ding, I Zharkov Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 98 | 2022 |
| Multitask learning for local seismic image processing: fault detection, structure-oriented smoothing with edge-preserving, and seismic normal estimation by using a single … X Wu, L Liang, Y Shi, Z Geng, S Fomel Geophysical Journal International 219 (3), 2097-2109, 2019 | 87 | 2019 |
| Deep learning for relative geologic time and seismic horizons Z Geng, X Wu, Y Shi, S Fomel Geophysics 85 (4), WA87-WA100, 2020 | 84 | 2020 |
| Deep relative geologic time: a deep learning method for simultaneously interpreting 3‐D seismic horizons and faults Z Bi, X Wu, Z Geng, H Li Journal of Geophysical Research: Solid Earth 126 (9), e2021JB021882, 2021 | 54 | 2021 |
| Deep learning for velocity model building with common-image gather volumes Z Geng, Z Zhao, Y Shi, X Wu, S Fomel, M Sen Geophysical Journal International 228 (2), 1054-1070, 2022 | 50 | 2022 |
| 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 34 (7), 3429-3443, 2022 | 32 | 2022 |
| Relative geologic time estimation using a deep convolutional neural network Z Geng, X Wu, Y Shi, S Fomel SEG International Exposition and Annual Meeting, D033S038R001, 2019 | 31 | 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 | 25 | 2023 |
| Deep learning for local seismic image processing: Fault detection, structure-oriented smoothing with edge-preserving, and slope estimation by using a single convolutional … X Wu, L Liang, Y Shi, Z Geng, S Fomel Seg technical program expanded abstracts 2019, 2222-2226, 2019 | 21 | 2019 |
| A multi-task learning method for relative geologic time, horizons, and faults with prior information and transformer J Yang, X Wu, Z Bi, Z Geng IEEE Transactions on Geoscience and Remote Sensing 61, 1-20, 2023 | 19 | 2023 |
| Relative time seislet transform Z Geng, X Wu, S Fomel, Y Chen Geophysics 85 (2), V223-V232, 2020 | 18 | 2020 |
| Semisupervised salt segmentation using mean teacher Z Geng, Z Hu, X Wu, L Liang, S Fomel Interpretation 10 (3), SE21-SE29, 2022 | 16 | 2022 |
| Building realistic structure models to train convolutional neural networks for seismic structural interpretation, Geophysics, 85, Wa27–Wa39 X Wu, Z Geng, Y Shi, N Pham, S Fomel, G Caumon WA27–WA39, 2020 | 12 | 2020 |
| Deep learning for enhancing multisource reverse time migration Y Li, X Jia, X Wu, Z Geng IEEE Transactions on Geoscience and Remote Sensing 60, 1-13, 2022 | 10 | 2022 |
| 3D relative geologic time estimation with deep learning Z Bi, Z Geng, H Gao, X Wu, H Li SEG International Exposition and Annual Meeting, D021S008R007, 2020 | 9 | 2020 |
| Simultaneous reconstruction and denoising of extremely sparse 5-D seismic data by a simple and effective method H Wang, Y Chen, YASI Oboué, R Abma, Z Geng, S Fomel, Y Chen IEEE Transactions on Geoscience and Remote Sensing 60, 1-12, 2021 | 8 | 2021 |
| LOUD: Local orthogonalization-constrained unsupervised deep-learning denoiser Z Geng, Y Chen, S Fomel, L Liang IEEE Transactions on Geoscience and Remote Sensing 60, 1-12, 2022 | 6 | 2022 |
| First-arrival phase-traveltime tomography Y Liu, Z Wu, Z Geng SEG 2017 Workshop: Full-waveform Inversion and Beyond, Beijing, China, 20-22 …, 2017 | 6 | 2017 |
| Relative geologic time estimation using a deep convolutional neural network: 89th Annual International Meeting, SEG, Expanded Abstracts, 2238–2242 Z Geng, X Wu, Y Shi, S Fomel Abstract, 2019 | 4 | 2019 |
Xinming WuProfessor of Geophysics, University of Science and Technology of ChinaPreverjeni e-poštni naslov na ustc.edu.cn
