In this paper, the complete process of constructing 3D digital core by full convolutional neural network is described carefully. A large number of sandstone computed tomography (CT) images are used as training input for a fully convolutional neural network model. This model is used to reconstruct the three-dimensional (3D) digital core of Berea sandstone based on a small number of CT images. The Hamming distance together with the Minkowski functions for porosity, average volume specific surface area, average curvature, and connectivity of both the real core and the digital reconstruction are used to evaluate the accuracy of the proposed method. The results show that the reconstruction achieved relative errors of 6.26%, 1.40%, 6.06%, and 4.91% for the four Minkowski functions and a Hamming distance of 0.04479. This demonstrates that the proposed method can not only reconstruct the physical properties of real sandstone but can also restore the real characteristics of pore distribution in sandstone, is the ability to which is a new way to characterize the internal microstructure of rocks.

本文详细介绍了通过全卷积神经网络构建3D数字核心的完整过程。大量砂岩计算机断层扫描（CT）图像用作全卷积神经网络模型的训练输入。该模型用于基于少量CT图像重建Berea砂岩的三维（3D）数字岩心。汉明距离与Minkowski函数一起用于孔隙率，平均体积比表面积，平均曲率以及实芯和数字重建的连通性均被用来评估该方法的准确性。结果表明，对于四个Minkowski函数，汉明距离为0.04479，重建的相对误差分别为6.26％，1.40％，6.06％和4.91％。