Super dimension reconstruction algorithm based on porosity classification

Abstract:

Since a lot of pattern matching in the process of dictionary set establishment and three-dimensional(3D) reconstruction, the super dimension algorithm takes a long time and is difficult in practical application. To solve this problem, a super-dimensional reconstruction algorithm based on porosity classification is proposed, which can greatly reduce the time cost of 3D reconstruction. First, the dictionary sets are classified by the porosity of dictionary elements. Then, the porosity classification dictionary is used to search according to porosity during reconstruction, and the corresponding dictionary interval is searched first. Based on the 3D reconstruction of different training images and porosity distribution, an adaptive search range determination method is proposed. Finally, the effectiveness of the proposed super dimension algorithm is verified by multiple reconstruction of the training images of high, medium and low porosity, a comparative analysis is made on the reconstruction results of the traditional Super dimension algorithm and the proposed algorithm in terms of the statistical characteristic function, pore-throat parameters and reconstruction time.