Hierarchical 3D reconstruction algorithm of core image based on detailed and structural dictionaries

The microstructure of porous media is of great significance to the study and analysis of their physical properties and mechanical properties. At present, the three-dimensional reconstruction method is widely used to obtain digital models of porous media microstructures that can be used to reconstruct their three-dimensional structures from two-dimensional training images. The super-dimensional algorithm, a learning-based method, makes full use of a priori three-dimensional structural information, and its reconstruction effect is better than that of the aforementioned traditional method. However, most current algorithms are based on 128-or 256-size training image reconstruction, which is still far from the actual application of the image size. To make algorithms more suitable to the actual application, we herein propose a hierarchical super-dimensional reconstruction method based on the idea of a multiple grid to complete the reconstruction of large-size images, such as the three-dimensional reconstruction of 512-or even 1000-size images. Using this method, we designed a template of the detailed information dictionary and developed a corresponding matching algorithm for a hierarchical reconstruction process. To further improve the efficiency of reconstruction, dictionary element porosity characteristics were used for classification. Finally, to verify the effectiveness of the proposed method, four groups of sandstone image reconstruction experiments were carried out, and the reference images with different sizes and porosity were reconstructed several times. Furthermore, to determine the accuracy of the reconstruction results, we compared the real structure with the visual and statistical indicators, and pore and throat parameters.