Reconstructing the 3D digital core with a fully convolutional neural network

作者：Li Qiong,Chen Zheng,He Jian-Jun,Hao Si-Yu,Wang Rui,Yang Hao-Tao,Sun Hua-Jun

摘要：In this paper, the complete process of constructing 3D digital core by fullconvolutional neural network is described carefully. A large number of sandstone computedtomography (CT) images are used as training input for a fully convolutional neural networkmodel. This model is used to reconstruct the three-dimensional (3D) digital core of Bereasandstone based on a small number of CT images. The Hamming distance together with theMinkowski functions for porosity, average volume specifi c surface area, average curvature,and connectivity of both the real core and the digital reconstruction are used to evaluate theaccuracy of the proposed method. The results show that the reconstruction achieved relativeerrors of 6.26%, 1.40%, 6.06%, and 4.91% for the four Minkowski functions and a Hammingdistance of 0.04479. This demonstrates that the proposed method can not only reconstructthe physical properties of real sandstone but can also restore the real characteristics of poredistribution in sandstone, is the ability to which is a new way to characterize the internalmicrostructure of rocks.

发文机构：College of Geophysics College of Information Science&Technology（College of Cybersecurity China Mobile Communications Group Sichuan Co.

关键词：Fullyconvolutionalneuralnetwork3Ddigitalcorenumericalsimulationtrainingset

分类号： TP3[自动化与计算机技术—计算机科学与技术]