论文标题
使用空间组装的生成对抗神经网络合成的快速，可扩展的地球纹理合成
Fast and Scalable Earth Texture Synthesis using Spatially Assembled Generative Adversarial Neural Networks
论文作者
论文摘要
具有复杂形态几何形状和成分（例如页岩和碳酸盐岩石）的地球质地通常以稀疏的田间样品来表征，这是由于昂贵且耗时的表征过程。因此，以低计算成本以相似的拓扑结构产生任意大小的地质纹理已成为现实的地产重建的关键任务之一。最近，生成的对抗神经网络（GAN）表现出了合成输入质地图像并创建均衡的地材料图像的潜力。但是，与gans框架的纹理合成通常受到输出纹理大小的计算成本和可扩展性的限制。在这项研究中，我们提出了一个空间组装的gan（sagans），可以生成任意大尺寸的输出图像，无论训练效率的训练图像的大小如何。用两维（2D和3D）岩石图像样品评估了萨根人的性能，广泛用于地球纹理的地列表重建。我们证明，萨根人可以产生与训练图像相似的连通性和结构属性的任意统计实现，即使在单个训练图像上也可以产生各种实现。此外，与标准gans框架相比，计算时间显着改善。
The earth texture with complex morphological geometry and compositions such as shale and carbonate rocks, is typically characterized with sparse field samples because of an expensive and time-consuming characterization process. Accordingly, generating arbitrary large size of the geological texture with similar topological structures at a low computation cost has become one of the key tasks for realistic geomaterial reconstruction. Recently, generative adversarial neural networks (GANs) have demonstrated a potential of synthesizing input textural images and creating equiprobable geomaterial images. However, the texture synthesis with the GANs framework is often limited by the computational cost and scalability of the output texture size. In this study, we proposed a spatially assembled GANs (SAGANs) that can generate output images of an arbitrary large size regardless of the size of training images with computational efficiency. The performance of the SAGANs was evaluated with two and three dimensional (2D and 3D) rock image samples widely used in geostatistical reconstruction of the earth texture. We demonstrate SAGANs can generate the arbitrary large size of statistical realizations with connectivity and structural properties similar to training images, and also can generate a variety of realizations even on a single training image. In addition, the computational time was significantly improved compared to standard GANs frameworks.
