利用改进的生成对抗网络生成具有多样性保真度和训练图像统计特性的地质相模型
Generating Geological Facies Models with Fidelity to Diversity and Statistics of Training Images using Improved Generative Adversarial Networks
摘要：
本文提出了一种方法和工作流程，克服了传统的生成对抗网络（GANs）的地质相建模的局限性。它试图通过可解释的潜在向量来提高训练的稳定性，并保证生成的地质的多样性。确保所得样本具有与训练数据集相等的概率（或无偏分布）。这在应用GANs生成无偏和有代表性的地质模型时至关重要，这些模型可进一步用于促进油田勘探和开发中的客观不确定性评估和最佳决策。我们提出并实现了一种新的GANs变体，称为Info-WGAN，用于地质相建模，该变体将信息最大化生成对抗网络（InfoGAN）与Wasserstein距离和梯度惩罚（GP）相结合，用于学习可解释的潜在代码，并从训练数据中生成稳定且无偏的分布。与原有的GAN设计不同，InfoGAN可以使用带有完整、部分或无标签的训练图像，对训练数据集中表现出的复杂沉积类型进行解缠，实现生成样本的多样性和多样性。这是通过添加额外的分类变量来实现的，这些变量除了在原始GANs中使用的仅仅是随机化的潜在向量之外，还提供了分离的语义表示。通过这种方式，正则化项被用于最大化这种潜在分类代码与损失函数中的生成的地质相之间的互信息。此外，通过Info-WGAN得到的无偏采样使得在地质建模中的数据调节比传统的GANs容易得多，因为多样性和多样性以及生成器的无条件采样的等概率。
英文摘要：
This paper presents a methodology and workflow that overcome the limitations of the conventional Generative Adversarial Networks (GANs) for geological facies modeling. It attempts to improve the training stability and guarantee the diversity of the generated geology through interpretable latent vectors. The resulting samples are ensured to have the equal probability (or an unbiased distribution) as from the training dataset. This is critical when applying GANs to generate unbiased and representative geological models that can be further used to facilitate objective uncertainty evaluation and optimal decision-making in oil field exploration and development. We proposed and implemented a new variant of GANs called Info-WGAN for the geological facies modeling that combines Information Maximizing Generative Adversarial Network (InfoGAN) with Wasserstein distance and Gradient Penalty (GP) for learning interpretable latent codes as well as generating stable and unbiased distribution from the training data. Different from the original GAN design, InfoGAN can use the training images with full, partial, or no labels to perform disentanglement of the complex sedimentary types exhibited in the training dataset to achieve the variety and diversity of the generated samples. This is accomplished by adding additional categorical variables that provide disentangled semantic representations besides the mere randomized latent vector used in the original GANs. By such means, a regularization term is used to maximize the mutual information between such latent categorical codes and the generated geological facies in the loss function. Furthermore, the resulting unbiased sampling by Info-WGAN makes the data conditioning much easier than the conventional GANs in geological modeling because of the variety and diversity as well as the equal probability of the unconditional sampling by the generator.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
