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Updated: May 14, 2026
07:33
Quantifying Mixing using Magnetic Resonance Imaging
Published on: January 25, 2012
Grain sizing in porous media using Bayesian magnetic resonance
D J Holland1, J Mitchell, A Blake
1Department of Chemical Engineering and Biotechnology, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, United Kingdom. djh79@cam.ac.uk
Physical Review Letters
|February 7, 2013
Summary
We developed a new Bayesian method to analyze magnetic resonance data for granular solids. This approach noninvasively characterizes grain and pore sizes in porous media, like rock cores, below imaging resolution.
Area of Science:
- Geophysics
- Materials Science
- Statistical Physics
Background:
- Magnetic resonance (MR) is typically used to image structures by acquiring k-space data and performing Fourier transforms.
- Characterizing granular solids and porous media often requires understanding grain and pore size distributions.
- Noninvasive techniques are crucial for analyzing the internal structure of materials like rock cores.
Purpose of the Study:
- To introduce a novel Bayesian inference approach for analyzing magnetic resonance data of granular solids.
- To develop a method for characterizing grain sizes in porous media below the resolution limits of traditional magnetic resonance imaging.
- To calculate pore size distributions from measured grain size distributions using a Monte Carlo approach.
Main Methods:
- Utilizing the Rayleigh distribution of signal intensity in k-space for Bayesian analysis.
- Defining a likelihood function based on observed signal intensity distributions.
- Applying a Monte Carlo method to derive pore size distributions from grain size data.
Main Results:
- Successfully applied a Bayesian inference approach to magnetic resonance data of granular solids.
- Demonstrated noninvasive characterization of grain sizes on length scales below MR imaging resolution.
- Calculated pore size distributions from derived grain size distributions in water-saturated rock cores.
Conclusions:
- The proposed Bayesian method offers a powerful tool for characterizing granular solids and porous media.
- This technique enables noninvasive analysis of microstructural properties not achievable with conventional magnetic resonance imaging.
- The approach is validated with practical examples using water-saturated rock cores.
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