A stable deep adversarial learning approach for geological facies
generation
- URL: http://arxiv.org/abs/2305.13318v3
- Date: Mon, 4 Mar 2024 14:31:05 GMT
- Title: A stable deep adversarial learning approach for geological facies
generation
- Authors: Ferdinand Bhavsar, Nicolas Desassis, Fabien Ors, Thomas Romary
- Abstract summary: Deep generative learning is a promising approach to overcome the limitations of traditional geostatistical simulation models.
This research aims to investigate the application of generative adversarial networks and deep variational inference for conditionally meandering channels in underground volumes.
- Score: 32.97208255533144
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The simulation of geological facies in an unobservable volume is essential in
various geoscience applications. Given the complexity of the problem, deep
generative learning is a promising approach to overcome the limitations of
traditional geostatistical simulation models, in particular their lack of
physical realism. This research aims to investigate the application of
generative adversarial networks and deep variational inference for
conditionally simulating meandering channels in underground volumes. In this
paper, we review the generative deep learning approaches, in particular the
adversarial ones and the stabilization techniques that aim to facilitate their
training. The proposed approach is tested on 2D and 3D simulations generated by
the stochastic process-based model Flumy. Morphological metrics are utilized to
compare our proposed method with earlier iterations of generative adversarial
networks. The results indicate that by utilizing recent stabilization
techniques, generative adversarial networks can efficiently sample from target
data distributions. Moreover, we demonstrate the ability to simulate
conditioned simulations through the latent variable model property of the
proposed approach.
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