| FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation X Wu, L Liang, Y Shi, S Fomel Geophysics 84 (3), IM35-IM45, 2019 | 681 | 2019 |
| SaltSeg: Automatic 3D salt segmentation using a deep convolutional neural network Y Shi, X Wu, S Fomel Interpretation 7 (3), SE113-SE122, 2019 | 177 | 2019 |
| 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 | 175 | 2020 |
| Convolutional neural networks for fault interpretation in seismic images X Wu, Y Shi, S Fomel, L Liang SEG International Exposition and Annual Meeting, SEG-2018-2995341, 2018 | 163* | 2018 |
| Applications of supervised deep learning for seismic interpretation and inversion Y Zheng, Q Zhang, A Yusifov, Y Shi The Leading Edge 38 (7), 526-533, 2019 | 126 | 2019 |
| FaultNet3D: Predicting fault probabilities, strikes, and dips with a single convolutional neural network X Wu, Y Shi, S Fomel, L Liang, Q Zhang, AZ Yusifov IEEE Transactions on Geoscience and Remote Sensing 57 (11), 9138-9155, 2019 | 125 | 2019 |
| Automatic salt-body classification using a deep convolutional neural network Y Shi, X Wu, S Fomel SEG International Exposition and Annual Meeting, SEG-2018-2997304, 2018 | 116* | 2018 |
| 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 | 71 | 2019 |
| Deep learning for relative geologic time and seismic horizons Z Geng, X Wu, Y Shi, S Fomel Geophysics 85 (4), WA87-WA100, 2020 | 70 | 2020 |
| Waveform embedding: Automatic horizon picking with unsupervised deep learning Y Shi, X Wu, S Fomel Geophysics 85 (4), WA67-WA76, 2020 | 58 | 2020 |
| FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation, Geophysics, 84, IM35–IM45 X Wu, L Liang, Y Shi, S Fomel IM35–IM45, 2019 | 44 | 2019 |
| 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 | 38 | 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 | 23 | 2019 |
| FaultNet: A deep CNN model for 3D automated fault picking Q Zhang, A Yusifov, C Joy, Y Shi, X Wu SEG International Exposition and Annual Meeting, D043S136R003, 2019 | 21 | 2019 |
| 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 | 19 | 2019 |
| Incremental correlation of multiple well logs following geologically optimal neighbors X Wu, Y Shi, S Fomel, F Li Interpretation 6 (3), T713-T722, 2018 | 19 | 2018 |
| Deep learning parameterization for geophysical inverse problems Y Shi, X Wu, S Fomel SEG 2019 Workshop: Mathematical Geophysics: Traditional vs Learning, Beijing …, 2020 | 14 | 2020 |
| Interactively tracking seismic geobodies with a deep-learning flood-filling network Y Shi, X Wu, S Fomel Geophysics 86 (1), A1-A5, 2021 | 11 | 2021 |
| Finding an optimal well-log correlation sequence using coherence-weighted graphs Y Shi, X Wu, S Fomel SEG Technical Program Expanded Abstracts 2017, 1982-1987, 2017 | 11 | 2017 |
| Predicting road accident risk using geospatial data and machine learning (demo paper) Y Shi, R Biswas, M Noori, M Kilberry, J Oram, J Mays, S Kharude, D Rao, ... Proceedings of the 29th International Conference on Advances in Geographic …, 2021 | 8 | 2021 |
Xinming WuProfessor of Geophysics, University of Science and Technology of ChinaVerified email at ustc.edu.cn
