We propose a novel deep learning architecture for three-dimensional porous media structure reconstruction from two-dimensional slices. A high-level idea is that we fit a distribution on all possible three-dimensional structures of a specific type based on the given dataset of samples. Then, given partial information (central slices) we recover the three-dimensional structure that is built around such slices. Technically, it is implemented as a deep neural network with encoder, generator and discriminator modules. Numerical experiments show that this method gives a good reconstruction in terms of Minkowski functionals.
翻译:我们提出一个新的深层学习结构,从二维片段重建三维多孔媒体结构。一个高层次的想法是,我们根据给定的样本数据集,将所有可能的三维结构分布在特定类型的三维结构上。然后,根据部分信息(中央片段),我们恢复了围绕这些片段建造的三维结构。从技术上讲,它是作为带有编码器、生成器和歧视器模块的深层神经网络实施的。数字实验表明,这种方法在Minkowski功能方面提供了良好的重建。