  We present a new technique, based on semivariogram methodology, for obtaining
point estimates for use in prior modeling for solving Bayesian inverse
problems. This method requires a connection between Gaussian processes with
covariance operators defined by the Mat\'ern covariance function and Gaussian
processes with precision (inverse-covariance) operators defined by the Green's
functions of a class of elliptic stochastic partial differential equations
(SPDEs). We present a detailed mathematical description of this connection. We
will show that there is an equivalence between these two Gaussian processes
when the domain is infinite -- for us, $\mathbb{R}^2$ -- which breaks down when
the domain is finite due to the effect of boundary conditions on Green's
functions of PDEs. We show how this connection can be re-established using
extended domains. We then introduce the semivariogram method for estimating the
Mat\'ern covariance parameters, which specify the Gaussian prior needed for
stabilizing the inverse problem. Results are extended from the isotropic case
to the anisotropic case where the correlation length in one direction is larger
than another. Finally, we consider the situation where the correlation length
is spatially dependent rather than constant. We implement each method in
two-dimensional image inpainting test cases to show that it works on practical
examples.
