Physics-driven deep learning joint inversion
Author:
Affiliation:
1. Saudi Aramco
2. Aramco Americas
3. Aramco Overseas Company
Publisher
Society of Exploration Geophysicists
Link
https://library.seg.org/doi/pdf/10.1190/segam2020-3424997.1
Reference11 articles.
1. Adler, J., and O. Öktem, 2017. Solving ill-posed inverse problems using iterative deep neural networks: Inverse Problems, 33, 1, 1–24.
2. Training with Noise is Equivalent to Tikhonov Regularization
3. A Generalized Representation of the Electromagnetic Fields In A Layered Earth
4. Deep-learning electromagnetic monitoring coupled to fluid flow simulators
5. High-resolution velocity modeling by seismic-airborne TEM joint inversion: A new perspective for near-surface characterization
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