AUTHOR=Coiffier Guillaume , Renard Philippe , Lefebvre Sylvain TITLE=3D Geological Image Synthesis From 2D Examples Using Generative Adversarial Networks JOURNAL=Frontiers in Water VOLUME=2 YEAR=2020 URL=https://www.frontiersin.org/journals/water/articles/10.3389/frwa.2020.560598 DOI=10.3389/frwa.2020.560598 ISSN=2624-9375 ABSTRACT=

Generative Adversarial Networks (GAN) are becoming an alternative to Multiple-point Statistics (MPS) techniques to generate stochastic fields from training images. But a difficulty for all the training image based techniques (including GAN and MPS) is to generate 3D fields when only 2D training data sets are available. In this paper, we introduce a novel approach called Dimension Augmenter GAN (DiAGAN) enabling GANs to generate 3D fields from 2D examples. The method is simple to implement and is based on the introduction of a random cut sampling step between the generator and the discriminator of a standard GAN. Numerical experiments show that the proposed approach provides an efficient solution to this long lasting problem.