  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.
