Parametric generation of conditional geological realizations using generative neural networks

We introduce a method for parametric generation of conditional geological realizations using generative neural networks. We build on our recent work where we trained a neural network to generate unconditional geological realizations using generative adversarial networks. Here we propose a method for post-hoc conditioning of pre-trained generator networks to generate conditional realizations. We frame the problem in the Bayesian setting and model the posterior distribution of the latent vector given observations. To efficiently generate multiple latent vector solutions, we train a neural network to generate samples from the posterior distribution. This inference network is trained by minimizing the discrepancy between its output distribution and the posterior. Once the inference network is trained, it is coupled to the (unconditional) generator to obtain the conditional generator, thus also maintaining a parametrization of the (conditional) generation process.