论文标题
使用生成的对抗网络生成无代表性的地质相的比例
Generating unrepresented proportions of geological facies using Generative Adversarial Networks
论文作者
论文摘要
在这项工作中，我们研究了生成对抗网络（GAN）在地质数据集中插值和推断相比例中的能力。假定具有未代表性（又称缺失）比例的新生成的实现属于相同的原始数据分布。具体来说，我们设计了一个有条件的剂量模型，该模型可以将生成的相驱动到训练集中找不到的新比例。提出的研究包括对各种培训环境和模型架构的研究。此外，我们设计了新的调节程序，以改善缺失的样品的生成。在二元相和多个相的图像上进行的数值实验表现出良好的地质一致性以及与目标条件的牢固相关性。
In this work, we investigate the capacity of Generative Adversarial Networks (GANs) in interpolating and extrapolating facies proportions in a geological dataset. The new generated realizations with unrepresented (aka. missing) proportions are assumed to belong to the same original data distribution. Specifically, we design a conditional GANs model that can drive the generated facies toward new proportions not found in the training set. The presented study includes an investigation of various training settings and model architectures. In addition, we devised new conditioning routines for an improved generation of the missing samples. The presented numerical experiments on images of binary and multiple facies showed good geological consistency as well as strong correlation with the target conditions.
