Deep‐Learning‐Based Inverse Modeling Approaches: A Subsurface Flow Example

Author:

Wang Nanzhe1,Chang Haibin1,Zhang Dongxiao23ORCID

Affiliation:

1. BIC‐ESAT, ERE, and SKLTCS College of Engineering Peking University Beijing P. R. China

2. School of Environmental Science and Engineering Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control Southern University of Science and Technology Shenzhen China

3. School of Environmental Science and Engineering State Environmental Protection Key Laboratory of Integrated Surface Water‐Groundwater Pollution Control Southern University of Science and Technology Shenzhen China

Funder

National Natural Science Foundation of China

Publisher

American Geophysical Union (AGU)

Subject

Space and Planetary Science,Earth and Planetary Sciences (miscellaneous),Geochemistry and Petrology,Geophysics

Reference65 articles.

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2. Solving ill-posed inverse problems using iterative deep neural networks

3. Anterion F. Eymard R. &Karcher B.(1989).Use of parameter gradients for reservoir history matching.Paper presented at the SPE Symposium on Reservoir Simulation.Houston TX:Society of Petroleum Engineers.https://doi.org/10.2118/18433-MS

4. Deep learning for photoacoustic tomography from sparse data

5. Optical tomography in medical imaging

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