Отправлено: 30.12.13 06:51. Заголовок: Grain Sizing in Porous Media using Bayesian Magnetic Resonance

Grain Sizing in Porous Media using Bayesian Magnetic Resonance
D. J. Holland,1,* J. Mitchell,1 A. Blake,2 and L. F. Gladden1
1
Department of Chemical Engineering and Biotechnology, University of Cambridge,
Pembroke Street, Cambridge CB2 3RA, United Kingdom 2
Microsoft Research, 7 J.J. Thompson Avenue, Cambridge CB3 0FB, United Kingdom
(Received 25 July 2012; revised manuscript received 17 October 2012; published 2 January 2013)

PRL 110, 018001 (2013) PHYSICAL REVIEW LETTERS week ending
4 JANUARY 2013
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