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3D Geological Image Synthesis from 2D Examples Using Generative Adversarial Networks
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, Petroleum Geostatistics 2019, Sep 2019, Volume 2019, p.1 - 5
Abstract
Recently, Generative Adversarial Networks (GAN) have been proposed as a potential alternative to Multipoint Statistics (MPS) to generate stochastic fields from a large set of training images. 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.