In the context of generating geological facies conditioned on observed data, samples corresponding to all possible conditions are not generally available in the training set and hence the generation of these realizations depends primary on the generalization capability of the trained generative model. The problem becomes more complex when applied on non-stationary fields. In this work, we investigate the problem of using Generative Adversarial Networks (GANs) models to generate non-stationary geological channelized patterns and examine the models generalization capability at new spatial modes that were never seen in the given training set. The developed training method based on spatial-conditioning allowed for effective learning of the correlation between the spatial conditions (i.e. non-stationary maps) and the realizations implicitly without using additional loss terms or solving optimization problems for every new given data after training. In addition, our models can be trained on 2D and 3D samples. The results on real and artificial datasets show that we were able to generate geologically-plausible realizations beyond the training samples and with a strong correlation with the target maps.

翻译：在基于观测数据生成地质相的条件下，训练集中通常无法涵盖所有可能条件对应的样本，因此这些实现的生成主要依赖于训练后的生成模型的泛化能力。当应用于非平稳场时，问题变得更加复杂。本研究探讨了利用生成对抗网络（GANs）模型生成非平稳地质河道化模式，并检验模型在训练集未曾出现的新空间模态上的泛化能力。所开发的空间条件训练方法能够有效学习空间条件（即非平稳图）与实现之间的隐式关联，无需在训练后针对每个新给定数据添加额外损失项或求解优化问题。此外，我们的模型可基于二维和三维样本进行训练。在真实与人工数据集上的结果表明，我们能够生成超越训练样本范围且与目标图强相关的地质学合理实现。