  Stochastic network modeling is often limited by high computational costs to
generate a large number of networks enough for meaningful statistical
evaluation. In this study, Deep Convolutional Generative Adversarial Networks
(DCGANs) were applied to quickly reproduce drainage networks from the already
generated network samples without repetitive long modeling of the stochastic
network model, Gibb's model. In particular, we developed a novel
connectivity-informed method that converts the drainage network images to the
directional information of flow on each node of the drainage network, and then
transform it into multiple binary layers where the connectivity constraints
between nodes in the drainage network are stored. DCGANs trained with three
different types of training samples were compared; 1) original drainage network
images, 2) their corresponding directional information only, and 3) the
connectivity-informed directional information. Comparison of generated images
demonstrated that the novel connectivity-informed method outperformed the other
two methods by training DCGANs more effectively and better reproducing accurate
drainage networks due to its compact representation of the network complexity
and connectivity. This work highlights that DCGANs can be applicable for high
contrast images common in earth and material sciences where the network,
fractures, and other high contrast features are important.

Geophysics;Machine Learning;Signal Processing;Machine Learning