Text-Guided Well Log-Constrained Realistic Subsurface Model Generation via Stable Diffusion

References

1. Aminzadeh, F., Burkhard, N., Long, J., Kunz, T. and Duclos, P. [1996] Three dimensional SEG/EAEG models—An update. The Leading Edge, 15(2), 131–134.
   [Google Scholar]
2. Billette, F. and Brandsberg-Dahl, S. [2005] The 2004 BP velocity benchmark. In: 67th EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, cp-1.
   [Google Scholar]
3. Deng, C., Feng, S., Wang, H., Zhang, X., Jin, P., Feng, Y., Zeng, Q., Chen, Y. and Lin, Y. [2022] OpenFWI: Large-scale Multi-structural Benchmark Datasets for Full Waveform Inversion. Advances in Neural Information Processing Systems, 35, 6007–6020.
   [Google Scholar]
4. Durall, R., Ghanim, A., Fernandez, M.R., Ettrich, N. and Keuper, J. [2023] Deep diffusion models for seismic processing. Computers & Geosciences, 177, 105377.
   [Google Scholar]
5. Ho, J., Jain, A. and Abbeel, P. [2020] Denoising diffusion probabilistic models. Advances in neural information processing systems, 33, 6840–6851.
   [Google Scholar]
6. Irons, T. [2007] Sigsbee2 models.
   [Google Scholar]
7. Kazei, V., Ovcharenko, O., Plotnitskii, P., Peter, D., Zhang, X. and Alkhalifah, T. [2021] Mapping full seismic waveforms to vertical velocity profiles by deep learning. Geophysics, 86(5), R711–R721.
   [Google Scholar]
8. Martin, G.S., Wiley, R. and Marfurt, K.J. [2006] Marmousi2: An elastic upgrade for Marmousi. The leading edge, 25(2), 156–166.
   [Google Scholar]
9. Ovcharenko, O., Kazei, V., Alkhalifah, T.A. and Peter, D.B. [2022] Multi-task learning for low-frequency extrapolation and elastic model building from seismic data. IEEE Transactions on Geo-science and Remote Sensing, 60, 1–17.
   [Google Scholar]
10. Ovcharenko, O., Kazei, V., Peter, D. and Alkhalifah, T. [2019] Style transfer for generation of realistically textured subsurface models. In: SEG International Exposition and Annual Meeting. SEG, D043S103R004.
    [Google Scholar]
11. von Platen, P., Patil, S., Lozhkov, A., Cuenca, P., Lambert, N., Rasul, K., Davaadorj, M. and Wolf, T. [2022] Diffusers: State-of-the-art diffusion models. https://github.com/huggingface/diffusers.
    [Google Scholar]
12. Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J. et al. [2021] Learning transferable visual models from natural language supervision. In: International conference on machine learning. PMLR, 8748–8763.
    [Google Scholar]
13. Rombach, R., Blattmann, A., Lorenz, D., Esser, P. and Ommer, B. [2022] High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 10684–10695.
    [Google Scholar]
14. Wang, F., Huang, X. and Alkhalifah, T.A. [2023] A prior regularized full waveform inversion using generative diffusion models. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–11.
    [Google Scholar]
15. Yang, F. and Ma, J. [2019] Deep-learning inversion: A next-generation seismic velocity model building method. Geophysics, 84(4), R583–R599.
    [Google Scholar]
16. Zhu, D., Fu, L., Kazei, V. and Li, W. [2023] Diffusion Model for DAS-VSP Data Denoising. Sensors, 23(20), 8619.
    [Google Scholar]