| Fluid and lithofacies prediction based on integration of well-log data and seismic inversion: A machine-learning approach L Zhao, C Zou, Y Chen, W Shen, Y Wang, H Chen, J Geng Geophysics 86 (4), M151-M165, 2021 | 44 | 2021 |
| Porosity prediction with uncertainty quantification from multiple seismic attributes using random forest C Zou, L Zhao, M Xu, Y Chen, J Geng Journal of Geophysical Research: Solid Earth 126 (7), e2021JB021826, 2021 | 38 | 2021 |
| Quantitative seismic characterization of source rocks in lacustrine depositional setting using the Random Forest method: An example from the Changjiang sag in East China Sea basin Z LuanXiao, LIU JinShui, YAO YunXia, Z Kai, MA JiQiang, ZOU CaiFeng, ... Chinese Journal of Geophysics 64 (2), 700-715, 2021 | 18 | 2021 |
| A comparison of machine learning methods to predict porosity in carbonate reservoirs from seismic-derived elastic properties C Zou, L Zhao, F Hong, Y Wang, Y Chen, J Geng Geophysics 88 (2), B101-B120, 2023 | 6 | 2023 |
| Deep Neural Helmholtz Operators for 3D elastic wave propagation and inversion C Zou, K Azizzadenesheli, ZE Ross, RW Clayton arXiv preprint arXiv:2311.09608, 2023 | 4 | 2023 |
| A random forest regressor based uncertainty quantification of porosity estimation from multiple seismic attributes C Zou, L Zhao, M Xu, Y Chen, J Geng SEG International Exposition and Annual Meeting, D011S066R003, 2021 | 3 | 2021 |
| 3D Seismic Waveform Modeling and Inversion With Neural Operators ZE Ross, C Zou, K Azizzadenesheli, RW Clayton AGU23, 2023 | | 2023 |
| Imaging the Northern Los Angeles Basins with Autocorrelations C Zou, RW Clayton AGU23, 2023 | | 2023 |
| Machine learning based fluids and lithofacies prediction based on the integration of well logging data and seismic inversion C Zou, L Zhao, Y Chen, Y Wang SEG 2020 Workshop: 2nd SEG Borehole Geophysics, Virtual, 26–27 November 2020 …, 2020 | | 2020 |
