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.

翻译：在不可观测区域中模拟地质相对多种地质科学应用至关重要。鉴于问题的复杂性，深度生成学习是克服传统地质统计学模拟模型局限性（尤其是其缺乏物理真实性）的一种有前景的方法。本研究旨在探究生成对抗网络与深度变分推断在地下曲折河道的条件模拟中的应用。本文综述了深度生成学习方法，特别是对抗性方法及其旨在促进训练的稳定化技术。所提出的方法在基于随机过程模型Flumy生成的二维和三维模拟数据上进行了测试。利用形态学指标将我们提出的方法与早期生成对抗网络的迭代版本进行比较。结果表明，通过采用最新的稳定化技术，生成对抗网络能够高效地从目标数据分布中采样。此外，我们通过所提出方法的潜变量模型特性，展示了执行条件模拟的能力。