Statistical Inference Over Persistent Homology Predicts Fluid Flow in Porous Media
Statistical Inference Over Persistent Homology Predicts Fluid Flow in Porous Media
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DOI：
10.1029/2019wr025171
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发表时间：
2019-11
影响因子：
5.4
通讯作者：
Chul Moon;S. Mitchell;J. Heath;M. Andrew
中科院分区：
文献类型：
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作者：
Chul Moon;S. Mitchell;J. Heath;M. Andrew
我们根据多孔材料的几何形状和连通性统计推断多孔材料的流体流动和输运性质，而不需要详细的描述。我们通过持久同源性来总结结构，然后使用图像分析和统计来确定结构的相似性。从长远来看，这可能会使岩石快速自动分类为已知的原型。我们首先计算材料子体积样本的二值化3D图像的持久同源性。持久性参数是从推断的材料界面的符号欧几里得距离，其捕获孔隙和颗粒的尺寸分布。每个持久性图被转换成一个图像矢量。我们通过计算图像相似性来推断结构相似性。对于每个图像向量，我们计算主成分来提取特征。我们拟合统计模型来估计材料的渗透率、弯曲度和各向异性。我们开发了一个结构相似性指数，以确定统计代表性的基本卷。
We statistically infer fluid flow and transport properties of porous materials based on their geometry and connectivity, without the need for detailed We summarize structure by persistent homology and then determines the similarity of structures using image analysis and statistics. Longer term, this may enable quick and automated categorization of rocks into known archetypes. We first compute persistent homology of binarized 3D images of material subvolume samples. The persistence parameter is the signed Euclidean distance from inferred material interfaces, which captures the distribution of sizes of pores and grains. Each persistence diagram is converted into an image vector. We infer structural similarity by calculating image similarity. For each image vector, we compute principal components to extract features. We fit statistical models to features estimates material permeability, tortuosity, and anisotropy. We develop a Structural SIMilarity index to determine statistical representative elementary volumes.