| A Maxwell's equations based deep learning method for time domain electromagnetic simulations P Zhang, Y Hu, Y Jin, S Deng, X Wu, J Chen IEEE Journal on Multiscale and Multiphysics Computational Techniques 6, 35-40, 2021 | 73 | 2021 |
| Progressive transfer learning for low-frequency data prediction in full-waveform inversion W Hu, Y Jin, X Wu, J Chen Geophysics 86 (4), R369-R382, 2021 | 56 | 2021 |
| A supervised descent learning technique for solving directional electromagnetic logging-while-drilling inverse problems Y Hu, R Guo, Y Jin, X Wu, M Li, A Abubakar, J Chen IEEE Transactions on Geoscience and Remote Sensing 58 (11), 8013-8025, 2020 | 53 | 2020 |
| Learn low wavenumber information in FWI via deep inception based convolutional networks Y Jin, W Hu, X Wu, J Chen SEG International Exposition and Annual Meeting, SEG-2018-2997901, 2018 | 46 | 2018 |
| A physics-driven deep-learning network for solving nonlinear inverse problems Y Jin, Q Shen, X Wu, J Chen, Y Huang Petrophysics 61 (01), 86-98, 2020 | 42 | 2020 |
| Seismic data denoising by deep-residual networks Y Jin, X Wu, J Chen, Z Han, W Hu SEG Technical Program Expanded Abstracts 2018, 4593-4597, 2018 | 41 | 2018 |
| A theory-guided deep neural network for time domain electromagnetic simulation and inversion using a differentiable programming platform Y Hu, Y Jin, X Wu, J Chen IEEE Transactions on Antennas and Propagation 70 (1), 767-772, 2021 | 27 | 2021 |
| Physics-guided self-supervised learning for low frequency data prediction in FWI W Hu, Y Jin, X Wu, J Chen SEG Technical Program Expanded Abstracts 2020, 875-879, 2020 | 25 | 2020 |
| Using a physics-driven deep neural network to solve inverse problems for LWD azimuthal resistivity measurements Y Jin, X Wu, J Chen, Y Huang SPWLA Annual Logging Symposium, D053S015R002, 2019 | 25 | 2019 |
| A progressive deep transfer learning approach to cycle-skipping mitigation in FWI W Hu, Y Jin, X Wu, J Chen SEG International Exposition and Annual Meeting, D033S076R007, 2019 | 24 | 2019 |
| Efficient progressive transfer learning for full-waveform inversion with extrapolated low-frequency reflection seismic data Y Jin, W Hu, S Wang, Y Zi, X Wu, J Chen IEEE Transactions on Geoscience and Remote Sensing 60, 1-10, 2021 | 20 | 2021 |
| Classifying cutting volume at shale shakers in real-time via video streaming using deep-learning techniques X Du, Y Jin, X Wu, Y Liu, X Wu, O Awan, J Roth, KC See, N Tognini, ... SPE Drilling & Completion 35 (03), 317-328, 2020 | 11 | 2020 |
| Efficient progressive transfer learning for low-frequency reflection seismic data prediction Y Jin, W Hu, X Wu, J Chen First International Meeting for Applied Geoscience & Energy, 777-781, 2021 | 8 | 2021 |
| Deep learning enhanced joint geophysical inversion for crosswell monitoring Y Hu, Y Jin, X Wu, J Chen, J Chen, Q Shen, Y Huang 2021 United States National Committee of URSI National Radio Science Meeting …, 2021 | 6 | 2021 |
| A deep learning enhanced full waveform inversion scheme Y Jin, Y Zi, W Hu, X Wu, J Chen 2021 International Applied Computational Electromagnetics Society Symposium …, 2021 | 5 | 2021 |
| A robust learning method for low-frequency extrapolation in GPR full waveform inversion Y Jin, Y Zi, W Hu, Y Hu, X Wu, J Chen IEEE Geoscience and Remote Sensing Letters 19, 1-5, 2022 | 4 | 2022 |
| RNN-based gradient prediction for solving magnetotelluric inverse problem Y Jin, Y Hu, X Wu, J Chen SEG International Exposition and Annual Meeting, D041S085R004, 2020 | 4 | 2020 |
| A physics-driven deep-learning inverse solver for subsurface sensing Y Hu, Y Jin, X Wu, J Chen 2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP …, 2020 | 4 | 2020 |
| Deep Learning Model for Classifying Cutting Volume at Shale Shakers in Real-Time via Video Streaming X Du, Y Jin, X Wu, Y Liu, X Wu, O Awan, J Roth, KC See, N Tognini, ... SPE/IADC Drilling Conference and Exhibition, D011S005R004, 2019 | 4 | 2019 |
| Deep learning-assisted real-time forward modeling of electromagnetic logging in complex formations L Yan, Y Jin, C Qi, P Yuan, S Wang, X Wu, Y Huang, J Chen IEEE Geoscience and Remote Sensing Letters 19, 1-5, 2022 | 3 | 2022 |
Xuqing WuUniversity of Houston在 uh.edu 的电子邮件经过验证
