Multiscale reconstruction of porous media based on multiple dictionaries learning

05/16/2022
by   Pengcheng Yan, et al.
0

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.

READ FULL TEXT

page 2

page 4

page 5

page 10

page 11

page 12

page 17

page 20

research
08/04/2023

Exploring the Effect of Sparse Recovery on the Quality of Image Superresolution

Dictionary learning can be used for image superresolution by learning a ...
research
11/05/2021

Concurrent multiscale analysis without meshing: Microscale representation with CutFEM and micro/macro model blending

In this paper, we develop a novel unfitted multiscale framework that com...
research
05/30/2023

BPF Algorithms for Multiple Source-Translation Computed Tomography Reconstruction

Micro-computed tomography (micro-CT) is a widely used state-of-the-art i...
research
06/04/2014

Multiscale Fields of Patterns

We describe a framework for defining high-order image models that can be...
research
04/21/2020

A Neural Process Approach for Probabilistic Reconstruction of No-Data Gaps in Lunar Digital Elevation Maps

With the advent of NASA's lunar reconnaissance orbiter (LRO), a large am...
research
06/01/2022

Residual Multiplicative Filter Networks for Multiscale Reconstruction

Coordinate networks like Multiplicative Filter Networks (MFNs) and BACON...
research
11/10/2021

The Impact of Changes in Resolution on the Persistent Homology of Images

Digital images enable quantitative analysis of material properties at mi...

Please sign up or login with your details

Forgot password? Click here to reset