DeePore：用于快速全面表征多孔材料的深度学习工作流程
DeePore: a deep learning workflow for rapid and comprehensive characterization of porous materials
摘要：
DeePore是一种深度学习工作流程，用于基于二值化显微断层扫描图像快速估计各种多孔材料属性。通过结合天然存在的多孔纹理，我们生成了17700个半真实的三维微观结构的多孔地质材料的大小为256^3体素和30个物理属性的每个样品使用相应的孔隙网络模型的物理模拟计算。接下来，基于数据集训练设计的前馈卷积神经网络（CNN），以在几分之一秒内估计多孔材料的若干形态、液压、电气和机械特性。为了微调CNN设计，我们测试了9种不同的训练场景，并选择了1418个测试样本中平均决定系数（R^2）最高的一个。此外，3个独立的合成图像，以及3个现实的层析成像图像已使用所提出的方法进行了测试，并将结果与孔隙网络建模和实验数据进行了比较，分别。与实验数据相比，测试的绝对磁导率具有约13%的相对误差，考虑到直接数值模拟方法（例如格子玻尔兹曼和有限体积）的准确性，这是值得注意的。由于其无量纲方法，该工作流程与任何物理尺寸的图像兼容，并且可用于通过对扫描整个几何形状的滑动窗口的模型输出进行平均来表征大规模3-D图像。
英文摘要：
DeePore is a deep learning workflow for rapid estimation of a wide range of porous material properties based on the binarized micro-tomography images. By combining naturally occurring porous textures we generated 17700 semi-real 3-D micro-structures of porous geo-materials with size of 256^3 voxels and 30 physical properties of each sample are calculated using physical simulations on the corresponding pore network models. Next, a designed feed-forward convolutional neural network (CNN) is trained based on the dataset to estimate several morphological, hydraulic, electrical, and mechanical characteristics of the porous material in a fraction of a second. In order to fine-tune the CNN design, we tested 9 different training scenarios and selected the one with the highest average coefficient of determination (R^2) equal to 0.885 for 1418 testing samples. Additionally, 3 independent synthetic images as well as 3 realistic tomography images have been tested using the proposed method and results are compared with pore network modelling and experimental data, respectively. Tested absolute permeabilities had around 13 % relative error compared to the experimental data which is noticeable considering the accuracy of the direct numerical simulation methods such as Lattice Boltzmann and Finite Volume. The workflow is compatible with any physical size of the images due to its dimensionless approach and can be used to characterize large-scale 3-D images by averaging the model outputs for a sliding window that scans the whole geometry.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
