Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 60, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2021.3066975
Keywords
Geology; Gallium nitride; Generators; Training; Data models; Probability; Pipelines; Generative adversarial networks (GANs); geological facies models; geological pattern; geomodeling; geophysics; probability maps
Categories
Funding
- National Natural Science Foundation of China [42072146]
- Stanford Center for Earth Resources Forecasting
- Stanford School of Earth, Energy, and Environmental Sciences
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This study improves the simulation method based on generative adversarial networks (GANs) to bridge the gap between remotely sensed geophysical information and geology. The generated geological facies models are realistic, diversified, and consistent with all input conditions.
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 improve conditional facies simulation based on GANs (GANSim), a new Earth model simulation method based on generative adversarial networks (GANs), to bridge the gap between remotely sensed geophysical information and geology, by introducing a specially designed loss function and an input architecture for probability maps representing geophysical interpretation. After training, the GANSim is then used to produce multiple geological facies models conditioned to the input geophysics-interpreted probability maps alone or together with input 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 the training data set and the knowledge of conditioning to inputs from human-defined explicit functions. Given the commonality of remotely sensed geophysical information, sparse measurements, and global features, GANSim should be applicable to many problems of geosciences.
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