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 | 765 | 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 | 201 | 2020 |
SaltSeg: Automatic 3D salt segmentation using a deep convolutional neural network Y Shi, X Wu, S Fomel Interpretation 7 (3), SE113-SE122, 2019 | 195 | 2019 |
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 | 173* | 2018 |
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 | 139 | 2019 |
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 | 133 | 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 | 125* | 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 | 80 | 2019 |
Deep learning for relative geologic time and seismic horizons Z Geng, X Wu, Y Shi, S Fomel Geophysics 85 (4), WA87-WA100, 2020 | 79 | 2020 |
Waveform embedding: Automatic horizon picking with unsupervised deep learning Y Shi, X Wu, S Fomel Geophysics 85 (4), WA67-WA76, 2020 | 60 | 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 | 49 | 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 | 43 | 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 | 26 | 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 | 22 | 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 | 21 | 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 | 21 | 2018 |
Interactively tracking seismic geobodies with a deep-learning flood-filling network Y Shi, X Wu, S Fomel Geophysics 86 (1), A1-A5, 2021 | 15 | 2021 |
Deep learning parameterization for geophysical inverse problems Y Shi, X Wu, S Fomel SEG 2019 Workshop: Mathematical Geophysics: Traditional vs Learning, Beijing …, 2020 | 15 | 2020 |
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 | 12 | 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 | 9 | 2021 |