Discover the research work of our Ph.D. student Charlie Garayt on simulating 2D geological models combining a deep generative adversarial network (GAN) and a Bayesian approach.
2D Stochastic Structural Geomodeling with Deep Generative Adversarial Networks
August 10, 2023 at 2:00 pm
Authors: Charlie GARAYT, Pierre-Marie Gibert and Jean LANGANAY (Geovariances); Samy BLUSSEAU (Center for Mathematical Morphology (CMM), Mines Paris, PSL University); Nicolas DESASSIS and Thomas ROMARY (Centre for Geosciences and Geoengineering, Mines Paris, PSL University).
Artificial intelligence (AI) through deep learning offers new opportunities to solve complex problems such as structural geological modeling. In this work, we propose to use deep generative adversarial network (GAN) to generate unconditional geological models trained on a synthetic training dataset. The GAN is then used in a Bayesian context to find geological models that honor constraints such as drillholes. This approach makes it possible to simulate different geological models associated with different geological contexts. Finally, the proposed method offers better management of uncertainties.