Generating unrepresented proportions of geological facies using Generative Adversarial Networks. (arXiv:2203.09639v1 [cs.LG])

In this work, we investigate the capacity of Generative Adversarial Networks
(GANs) in interpolating and extrapolating facies proportions in a geological
dataset. The new generated realizations with unrepresented (aka. missing)
proportions are assumed to belong to the same original data distribution.
Specifically, we design a conditional GANs model that can drive the generated
facies toward new proportions not found in the training set. The presented
study includes an investigation of various training settings and model
architectures. In addition, we devised …

arxiv generative adversarial networks networks