We review methods to estimate the average crystal (grain) size and the
crystal (grain) size distribution in solid rocks. Average grain sizes
often provide the base for stress estimates or rheological calculations
requiring the quantification of grain sizes in a rock's microstructure.
The primary data for grain size data are either 1D (i.e. line intercept
methods), 2D (area analysis) or 3D (e.g., computed tomography, serial
sectioning). These data have been used for different data treatments
over the years, whereas several studies assume a certain probability
function (e.g., logarithm, square root) to calculate statistical
parameters as the mean, median, mode or the skewness of a crystal size
distribution. The finally calculated average grain sizes have to be
compatible between the different grain size estimation approaches in
order to be properly applied, for example, in paleo-piezometers or grain
size sensitive flow laws. Such compatibility is tested for different
data treatments using one- and two-dimensional measurements. We propose
an empirical conversion matrix for different datasets. These conversion
factors provide the option to make different datasets compatible with
each other, although the primary calculations were obtained in different
ways. In order to present an average grain size, we propose to use the
area-weighted and volume-weighted mean in the case of unimodal grain
size distributions, respectively, for 2D and 3D measurements. The shape
of the crystal size distribution is important for studies of nucleation
and growth of minerals. The shape of the crystal size distribution of
garnet populations is compared between different 2D and 3D measurements,
which are serial sectioning and computed tomography. The comparison of
different direct measured 3D data; stereological data and direct
presented 20 data show the problems of the quality of the smallest grain
sizes and the overestimation of small grain sizes in stereological
tools, depending on the type of CSD. (C) 2011 Published by Elsevier Ltd.