Generation of non-stationary stochastic fields using Generative Adversarial Networks with limited training data. (arXiv:2205.05469v1 [cs.LG])

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 …

arxiv data generation generative adversarial networks networks stochastic training training data