Bridging the gap between geophysics and geology with Generative Adversarial Networks (GANs)

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Suihong Song, Tapan Mukerji, Jiagen Hou


Inverse mapping from geophysics to geology is a difficult problem due to the inherent uncertainty of geophysical data and the spatially heterogeneous patterns (structure) in geology. We describe GANSim, a type of generative adversarial networks (GANs) that discovers the mapping between remotely-sensed geophysical information and geology with realistic patterns, with a specially designed loss function and an input architecture for geophysics-interpreted probability maps. This GANSim is then used to produce realizations of realistic geological facies models conditioned to the probability maps alone or together with well observations and global features. By evaluation, the generated facies models are realistic, diversified, and consistent with all input conditions. We demonstrate that the GAN learns the implicit geological pattern knowledge from training data and the knowledge of conditioning to inputs from human-defined explicit functions. Given the commonality of probability maps, sparse measurements, and global features, GANSim should be applicable to many problems of geosciences.



Artificial Intelligence and Robotics, Computer Sciences, Databases and Information Systems, Earth Sciences, Environmental Monitoring, Environmental Sciences, Geology, Geomorphology, Geophysics and Seismology, Natural Resource Economics, Oil, Gas, and Energy, Other Earth Sciences, Physical Sciences and Mathematics, Sedimentology, Water Resource Management


Channels, Conditional modeling, Deep learning, GANs, Generative Adversarial Networks, Geological facies modeling, Geological patterns, Geophysical interpretation, Inverse mapping, Knowledge learning, Pattern recognition, Probability maps, Reservoir prediction, Sedimentology prediction


Published: 2020-08-16 15:35


Academic Free License (AFL) 3.0