Deep Generative Seismic Inversion for Geological Facies

References

1. [1]Azevedo, L., Soares, A. (2017). Geostatistical Methods for Reservoir Modelling. Springer.
   [Google Scholar]
2. [2]Mosser, L., Dubrule, O., Blunt, MJ (2018). Conditioning of three-dimensional generative adversarial networks for pore and reservoir scale models. arXiv preprint arXiv:1802.05622
   [Google Scholar]
3. [3]Chan, S., Elsheikh, A.H.: Parametric generation of conditional geological realizations using generative neural networks.Comput. Geosci.23, 925–952 (2019)
   [Google Scholar]
4. [4]Dupont, E., Zhang, T., Tilke, P., Liang, L., Bailey, W. (2018). Generating realistic geology conditioned on physical measurements with generative adversarial networks. arXiv preprint arXiv: 1802.03065
   [Google Scholar]
5. [5]IsolaP, ZhuJ, ZhouT, EfrosAA (2017). Image-to-image translation with conditional adversarial networks.IEEE Conf Comput Vis Pattern Recogn (CVPR) 2017:5967–5976
   [Google Scholar]
6. [6]Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. In: The twenty-eighth annual conference on neural information processing systems (NIPS), Montréal, Canada (2014)
   [Google Scholar]
7. [7]Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S. (2017). GANs Trained by a two time-scale update rule converge to a local Nash Equilibrium. arXiv:1706.08500
   [Google Scholar]
8. [8]Antunes, B. (2023). Unpublished manuscript.
   [Google Scholar]