title: Grain sizing in porous media using Bayesian magnetic resonance.
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.
authors: Department of Chemical Engineering and Biotechnology, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, United Kingdom. djh79@cam.ac.uk
authors: D J
authors: DJ
authors: Holland
authors: J
authors: J
authors: Mitchell
authors: A
authors: A
authors: Blake
authors: L F
authors: LF
authors: Gladden
mesh_terms: D001499:Bayes Theorem; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008956:Models, Chemical; D009010:Monte Carlo Method; D010316:Particle Size; D016062:Porosity