3D stochastic reconstruction of porous media based on attention mechanisms and residual networks

Because of the complex internal porous structures, the reconstruction of porous media has encountered many difficulties in practical research and development. At present, numerical simulation methods are widely used in the field of reconstructing porous media, which can reconstruct results similar to the true pore structure, but generally are CPU-intensive and time-consuming. Recently, with the rapid development of deep learning, the powerful ability of feature extraction and structure prediction owned by deep learning can be transferred to reconstruct porous media. The convolutional neural network (CNN) is one of the classical methods in deep learning. A traditional CNN is unable to specially focus on the effective features in learning and possibly has degradation problem with the increase of layers. To address the degradation problem and make CNNs extract important features, residual networks and attention mechanisms are combined in CNNs to respectively alleviate network degradation and focus on the important features in the reconstruction of porous media. Besides, the bilinear interpolation is used to enlarge the size of compressed data. Compared with some traditional numerical reconstruction methods, CNN, GAN and some variants of GAN, our method has shown its practicability and effectiveness.