Improving patch-based simulation using Generative Adversial Networks

Abstract

Multiple-Point Simulation (MPS) is a geostatistical simulation technique commonly used to model complex geological patterns and subsurface heterogeneity. There have been a great variety of implementation methods developed within MPS, of which Patch-Based Simulation is a more recently developed class. While we have witnessed great progress in Patch-Based algorithms lately, they are still faced with two challenges: conditioning to point data and the occurrence of verbatim copy. Both of them are partly due to finite size of Training image, from which a limited size of pattern database is constructed. To address these questions, we propose a novel approach that we call Generative-Patched-Simulation (GPSim), which is based on Generative Adversarial Networks (GAN). With this method, we are able to generate sufficient (in theory an infinite) number of new patches based on the current pattern database. As demonstrated by the results on a simple 2D binary image, this approach shows its potential to address the two issues and thus improve Patch-based Simulation methods.