Generation of non-stationary stochastic fields using Generative Adversarial Networks

Abstract

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 using Generative Adversarial Networks (GANs) models to generate non-stationary geological channelized patterns and examine the models generalization capability at new spatial modes that were never seen in the given training set. The developed training method based on spatial-conditioning 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 optimization problems for every new given data after training. In addition, our models can be trained on 2D and 3D samples. The results on real and artificial datasets show that we were able to generate geologically-plausible realizations beyond the training samples and with a strong correlation with the target maps.

References in corpus (10)

• Conditional Generative Adversarial Nets
• Large Scale GAN Training for High Fidelity Natural Image Synthesis
• Spectral Normalization for Generative Adversarial Networks
• Reconstruction of three-dimensional porous media using generative adversarial neural networks
• Training-image based geostatistical inversion using a spatial generative adversarial neural network
• Parametric generation of conditional geological realizations using generative neural networks
• Discovering state-parameter mappings in subsurface models using generative adversarial networks
• Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks
• Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks
• Generating unrepresented proportions of geological facies using Generative Adversarial Networks