Aghamiry, H. S., Gholami, A., and Operto, S., 2019, Implementing bound constraints and total-variation regularization in extended full-waveform inversion with the alternating direction method of multiplier: Application to large contrast media. Geophysical Journal International, 218(2), 855–872.
Araya-Polo, M., Jennings, J., Adler, A. and Dahlke, T., 2018, Deep-learning tomography. Leading Edge, 37(1), 58–66.
Deutsch, C.V. and Journel, A.G., 1992, GSLIB: Geostatistical software library and user's guide, Oxford Univ Press, New York. 340.
Fang, J., Zhou, H., Elita Li, Y., Zhang, Q., Wang, L., Sun, P. and Zhang, J., 2020, Data-driven low-frequency signal recovery using deep-learning predictions in full-waveform inversion. Geophysics, 85(6), A37–A43.
Herrmann, F. J., A. Siahkoohi, and G. Rizzuti. Learned imaging with constraints and uncertainty quantification. In Neural Information Processing Systems (NeurIPS), 2019, Deep Inverse Workshop.
Johnson, J., Alahi, A. and Fei-Fei, L., 2016, Perceptual losses for real-time style transfer and super-resolution, European conference on computer vision. Springer, pp. 694–711.
Kaur, H., Pham, N., & Fomel, S., 2021, Seismic data interpolation using deep learning with generative adversarial networks. Geophysical Prospecting, 69(2), 307–326.
Laloy, E., Linde, N., Ruffino, C., Hrault, R., Gasso, G. and Jacques, D., 2019, Gradientbased deterministic inversion of geophysical data with generative adversarial networks: Is it feasible? Computers & Geosciences, 133, 104333.
Lecun, Y., Bengio, Y., & Hinton, G., 2015, Deep learning. Nature, 521(7553), 436–444.
Li, S., Liu, B., Ren, Y., Chen, Y., Yang, S., Wang, Y. and Jiang, P. (2020, Deep-Learning Inversion of Seismic Data. IEEE Transactions on Geoscience and Remote Sensing, 58(3), 2135–2149.
Mao, B., Han, L. G., Feng, Q. and Yin, Y. C. (2019). Subsurface velocity inversion from deep learning-based data assimilation. Journal of Applied Geophysics, 167, 172–179.
Ovcharenko, O. V., Kazei, D. P. and Alkhalifah, T., 2019, Style transfer for generation of realistically textured subsurface models: 89th Annual International Meeting, SEG, Expanded Abstracts, doi: 10.1190/segam2019- 3216349.1.
Ren, Y., Xu, X., Yang, S., Nie, L. and Chen, Y., 2020, A Physics-Based Neural-Network Way to Perform Seismic Full Waveform Inversion. IEEE Access, 8, 112266–112277.
Rezaee, H., D. Marcotte, P. Tahmasebi, and Saucier, A., 2015, Multiple-point geostatistical simulation using enriched pattern databases, Stochastic Environ. Res. Risk Assess., 29(3), 893–913
Siahkoohi, A., Rizzuti, G. and Herrmann, F. J., Deep bayesian inference for seismic imaging with tasks. arXiv preprint arXiv:2110.04825, 2021.
Siwei Yu, Jianwei Ma, and Wenlong Wang, 2019, Deep learning for denoisingdeep learning for denoising. Geophysics, 84(6):V333–V350.
Shihang Feng, Youzuo Lin, and Brendt Wohlberg, 2021, Multiscale data-driven seismic full-waveform inversion with field data study. IEEE Transactions on Geoscience and Remote Sensing, pp. 1–14.
Soares, A., 2001, Direct Sequential Simulation and Cosimulation, Mathematical Geology, 33, 911-926.
Tran, T.T., Deutsch, C.V. and Xie, Y., 2001, Direct Geostatistical Simulation With Multiscale Well, Seismic, and Production Data, SPE Annual Technical Conference and Exhibition, Vol. SPE Paper Number 71323. Society of Petroleum Engineers.
Wu, Y. and Lin, Y., 2019, InversionNet: An Efficient and Accurate Data-Driven Full Waveform Inversion. IEEE Transactions on Computational Imaging, 6, 419–433.
Yang, F. and Ma, J., 2019, Deep-learning inversion: A next-generation seismic velocity model building method. Geophysics, 84(4), R583–R599.
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