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Updated: May 14, 2026

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
Abstract:
We introduce a Bayesian inference approach to analyze magnetic resonance data of granular solids. To characterize structure using magnetic resonance, it is usual to acquire data in k space which are then Fourier transformed to obtain an image. An alternative approach, adopted here, is to utilize the Rayleigh distribution observed in the signal intensity for a given k when a random selection of grains is measured in k space, to define a likelihood function for Bayesian analysis. This Bayesian likelihood function is used to noninvasively characterize grains within a porous medium on length scales below the practical resolution of magnetic resonance imaging. A pore size distribution is then calculated from the measured grain size distribution using a Monte Carlo approach. We demonstrate this general technique with specific examples of water-saturated rock cores.
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