In the context of generating geological facies conditioned on observed data,
samples corresponding to all possible conditions are not generally available in
the training set and hence the generation of these realizations depends primary
on the generalization capability of the trained generative model. The problem
becomes more complex when applied on non-stationary fields. In this work, we
investigate the problem of training Generative Adversarial Networks (GANs)
models against a dataset of geological channelized patterns that has a few
non-stationary spatial modes and examine the training and self-conditioning
settings that improve the generalization capability at new spatial modes that
were never seen in the given training set. The developed training method
allowed for effective learning of the correlation between the spatial
conditions (i.e. non-stationary maps) and the realizations implicitly without
using additional loss terms or solving a costly optimization problem at the
realization generation phase. Our models, trained on real and artificial
datasets were able to generate geologically-plausible realizations beyond the
training samples with a strong correlation with the target maps