Reconstruction of three-dimensional porous media using multi-scale generative adversarial networks

Abstract

Numerical simulation methods have been widely used in the reconstruction of porous media, but their applicability usually suffers from the huge hardware burden and computational time. Recently, with the rapid development of deep learning, its powerful ability in feature extraction has been applied to reconstruct porous media. As an important branch of deep learning, generative adversarial network (GAN) has been considered as an effective method to extract features from training images (TIs). Based on GAN, this paper proposes a method for the reconstruction of porous media using multi-scale GAN (multi-GAN), which learns the features of the TI at different scales separately by using a convolutional pyramid structure. The proposed method uses only a single three-dimensional (3D) TI of porous media for reconstruction and the reconstructed images contain structural information of the TI at different scales. Compared with some typical methods, this method has certain advantages in terms of reconstruction quality and efficiency.