  The plurigaussian model is particularly suited to describe categorical
regionalized variables. Starting from a simple principle, the thresh-olding of
one or several Gaussian random fields (GRFs) to obtain categories, the
plurigaussian model is well adapted for a wide range ofsituations. By acting on
the form of the thresholding rule and/or the threshold values (which can vary
along space) and the variograms ofthe underlying GRFs, one can generate many
spatial configurations for the categorical variables. One difficulty is to
choose variogrammodel for the underlying GRFs. Indeed, these latter are hidden
by the truncation and we only observe the simple and cross-variogramsof the
category indicators. In this paper, we propose a semiparametric method based on
the pairwise likelihood to estimate the empiricalvariogram of the GRFs. It
provides an exploratory tool in order to choose a suitable model for each GRF
and later to estimate its param-eters. We illustrate the efficiency of the
method with a Monte-Carlo simulation study .The method presented in this paper
is implemented in the R packageRGeostats.
