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Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network
Here, we use a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media.
Training-image based geostatistical inversion using a spatial generative adversarial neural network
After training, realizations containing a few millions of pixels/voxels can be produced in a matter of seconds.
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