3D reconstruction of digital rocks based on StyleGAN and transformer

Abstract The 3D reconstruction of digital rocks assumes a paramount role within various engineering applications, necessitating careful consideration of the methods employed. The available approaches are divided into two groups, i.e., the physical experimental method and the numerical simulation method. Although the former is reliable, it incurs high costs and is limited by sample size constraints. The latter is cost-effective but suffers from prolonged processing times and sometimes suboptimal performance. However, the advent of deep learning has paved the way for integrating these techniques into 3D digital rock reconstruction. A standout amongst the method of deep learning techniques is the generative adversarial network (GAN). Nonetheless, existing models of GAN lack complete integration of multi-scale information. To address this issue, this study proposed style-transformer GAN (STGAN), a novel GAN model founded upon style-based GAN (StyleGAN) and Transformer. By utilizing the attention mechanisms of Transformer, STGAN ameliorates its ability to extract features from multi-scale training images. Additionally, the incorporation of style transfer further enhances the quality of the generated images. By comparing it with other deep learning algorithms, the efficiency and applicability of the proposed method are demonstrated.