Multiscale reconstruction of porous media based on multiple dictionaries learning

TL;DR

This paper introduces a multiscale reconstruction algorithm for porous media using multiple dictionaries learning, enabling accurate modeling of complex pore structures and properties across different scales.

Contribution

It presents a novel multiscale reconstruction method that integrates edge and micro-pore patterns from high-resolution data into low-resolution models.

Findings

Reconstructed pore structures closely match real high-resolution structures.

Geometric, topological, and permeability properties are preserved.

The method is effective regardless of input size.

Abstract

Digital modeling of the microstructure is important for studying the physical and transport properties of porous media. Multiscale modeling for porous media can accurately characterize macro-pores and micro-pores in a large-FoV (field of view) high-resolution three-dimensional pore structure model. This paper proposes a multiscale reconstruction algorithm based on multiple dictionaries learning, in which edge patterns and micro-pore patterns from homology high-resolution pore structure are introduced into low-resolution pore structure to build a fine multiscale pore structure model. The qualitative and quantitative comparisons of the experimental results show that the results of multiscale reconstruction are similar to the real high-resolution pore structure in terms of complex pore geometry and pore surface morphology. The geometric, topological and permeability properties of multiscale reconstruction results are almost identical to those of the real high-resolution pore structures. The experiments also demonstrate the proposal algorithm is capable of multiscale reconstruction without regard to the size of the input. This work provides an effective method for fine multiscale modeling of porous media.