Deep?Learning?Based Inverse Modeling Approaches: A Subsurface Flow Example
نویسندگان
چکیده
Deep-learning has achieved good performance and demonstrated great potential for solving forward inverse problems. In this work, two categories of innovative deep-learning-based modeling methods are proposed compared. The first category is deep-learning surrogate-based inversion methods, in which the Theory-guided Neural Network (TgNN) constructed as a surrogate problems with uncertain model parameters. By incorporating physical laws other constraints, TgNN can be limited simulation runs accelerate process significantly. Three proposed, including gradient method, Iterative Ensemble Smoother training method. second direct-deep-learning-inversion constrained geostatistical information, named TgNN-geo, framework direct modeling. neural networks introduced to approximate random parameters solution, respectively. order honor prior information parameters, network approximating trained by using observed or generated realizations. Then, minimizing loss function estimation approximation solution simultaneously obtained. Since incorporated, direct-inversion method based on TgNN-geo works well, even cases sparse spatial measurements imprecise statistics. Although general nature, thus applicable wide variety problems, they tested several subsurface flow It found that satisfactory results obtained high efficiency. Moreover, both advantages disadvantages further analyzed methods.
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ژورنال
عنوان ژورنال: Journal Of Geophysical Research: Solid Earth
سال: 2021
ISSN: ['2169-9356', '2169-9313']
DOI: https://doi.org/10.1029/2020jb020549