A stable deep adversarial learning approach for geological facies generation

arXiv:2305.13318v3 Announce Type: replace-cross
Abstract: The simulation of geological facies in an unobservable volume is essential in various geoscience applications. Given the complexity of the problem, deep generative learning is a promising approach to overcome the limitations of traditional geostatistical simulation models, in particular their lack of physical realism. This research aims to investigate the application of generative adversarial networks and deep variational inference for conditionally simulating meandering channels in underground volumes. In this paper, we review the generative deep …

abstract adversarial adversarial learning applications arxiv complexity cs.lg generative geoscience limitations physics.geo-ph simulation the simulation type