Parametric generation of conditional geological realizations using generative neural networks

13 Jul 2018 Shing Chan Ahmed H. Elsheikh

Deep learning techniques are increasingly being considered for geological applications where -- much like in computer vision -- the challenges are characterized by high-dimensional spatial data dominated by multipoint statistics. In particular, a novel technique called generative adversarial networks has been recently studied for geological parametrization and synthesis, obtaining very impressive results that are at least qualitatively competitive with previous methods... (read more)

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