  Stochastic image reconstruction is a key part of modern digital rock physics
and materials analysis that aims to create numerous representative samples of
material micro-structures for upscaling, numerical computation of effective
properties and uncertainty quantification. We present a method of
three-dimensional stochastic image reconstruction based on generative
adversarial neural networks (GANs). GANs represent a framework of unsupervised
learning methods that require no a priori inference of the probability
distribution associated with the training data. Using a fully convolutional
neural network allows fast sampling of large volumetric images.We apply a GAN
based workflow of network training and image generation to an oolitic Ketton
limestone micro-CT dataset. Minkowski functionals, effective permeability as
well as velocity distributions of simulated flow within the acquired images are
compared with the synthetic reconstructions generated by the deep neural
network. While our results show that GANs allow a fast and accurate
reconstruction of the evaluated image dataset, we address a number of open
questions and challenges involved in the evaluation of generative network-based
methods.
