It is common and convenient to treat distributed physical parameters as
Gaussian random fields and model them in an "inverse procedure" using
measurements of various properties of the fields. This article presents a
general method for this problem based on a flexible parameterization device
called "anchors", which captures local or global features of the fields. A
classification of all relevant data into two categories closely cooperates with
the anchor concept to enable systematic use of datasets of different sources
and disciplinary natures.
We comment on a recent approach to stochastic inversion, which centers on a
concept known as "anchors" and conducts nonparametric estimation of the
likelihood of the anchors (along with other model parameters) with respect to
data obtained from field processes. The method is called "anchored inversion"
or (less accurately) "method of anchored distribution". Conceptual and
technical observations are made regarding the development, interpretation, and
use of this approach.