论文标题
通过深度学习从图像中预测多孔介质的孔隙率，渗透率和曲折性
Predicting Porosity, Permeability, and Tortuosity of Porous Media from Images by Deep Learning
论文作者
论文摘要
卷积神经网络（CNN）用于编码障碍物的初始配置与多孔介质中的三个基本数量之间的关系：孔隙率（$φ$），渗透率$ k $和折磨（$ t $）。考虑了具有障碍的二维系统。通过晶格玻尔兹曼方法模拟了通过多孔培养基的流体流。证明CNN能够以良好的准确性来预测孔隙度，渗透率和曲折。随着CNN模型的使用，$ t $和$φ$之间的关系已被复制并与经验估计值进行了比较。已经对$φ\ in（0.37,0.99）$的系统进行了分析，该系统涵盖了五个数量级的渗透率$ k \ in（0.78，2.1 \ times 10^5）$ and Tortuosity $ t \ in（1.03,2.2.74）$。
Convolutional neural networks (CNN) are utilized to encode the relation between initial configurations of obstacles and three fundamental quantities in porous media: porosity ($φ$), permeability $k$, and tortuosity ($T$). The two-dimensional systems with obstacles are considered. The fluid flow through a porous medium is simulated with the lattice Boltzmann method. It is demonstrated that the CNNs are able to predict the porosity, permeability, and tortuosity with good accuracy. With the usage of the CNN models, the relation between $T$ and $φ$ has been reproduced and compared with the empirical estimate. The analysis has been performed for the systems with $φ\in (0.37,0.99)$ which covers five orders of magnitude span for permeability $k \in (0.78, 2.1\times 10^5)$ and tortuosity $T \in (1.03,2.74)$.
