Using generative adversarial networks to improve deep-learning fault interpretation networks

Abstract Deep learning is arguably one of the most important innovations in artificial intelligence in recent times. It allows for computational solutions to problems that are not easily characterized by a mathematical model or deterministic algorithm. It also allows for automated solutions to problems that are inherently subjective. Both of these criteria are endemic in the earth sciences, so innovative solutions to these challenges should be welcomed. We demonstrate a recent refinement to a deep-learning fault identification process that improves the continuity and compactness of predicted fault planes in areas where faults intersect. Historically, predictions from both deep learning and traditional algorithmic approaches were characterized by “blurry” clouds of intermediate probability values that extended well beyond the fault plane. To remediate this blurring problem and enhance confidence of inferences, we demonstrate a preprocessing technique in the image domain by using generative adversarial netw...