Deep-learning-based surrogate model for forward and inverse problems of wave propagation in porous media saturated with two fluids
Author:
Funder
National Key Research and Development Program of China
Hunan Provincial Natural Science Foundation of China
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Geophysics
Link
https://link.springer.com/content/pdf/10.1007/s11600-023-01206-1.pdf
Reference38 articles.
1. Alfarraj M, AlRegib G (2018) Petrophysical property estimation from seismic data using recurrent neural networks. In: SEG Technical Program Expanded Abstracts 2018 (pp. 2141–2146). Society of Exploration Geophysicists. https://doi.org/10.1190/segam2018-2995752.1
2. Ba J, Carcione JM, Nie J (2011) Biot‐Rayleigh theory of wave propagation in double‐porosity media. J Geophys Res Sol Ea 116 (B6). https://doi.org/10.1029/2010JB008185
3. Biot MA (1956a) Theory of elastic waves in a fluid-saturated porous solid. 1. Low frequency range. J Acoust Soc Am 28:168–178. https://doi.org/10.1121/1.1908239
4. Biot MA (1956b) Theory of propagation of elastic waves in a fluidsaturated porous solid. II. Higher frequency range. J Acoust Soc Am 28:179–191. https://doi.org/10.1121/1.1908241
5. Carcione JM, Picotti S (2006) P-wave seismic attenuation by slow wave diffusion: effects of inhomogeneous rock properties. Geophys 71:O1–O8. https://doi.org/10.1190/1.2194512
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