During the last decade multiple-point statistics has become in-creasingly popular as a tool for incorporating complex prior infor-mation when solving inverse problems in geosciences. A variety of methods have been proposed but often the implementation of these is not straightforward. One of these methods is the recently proposed Frequency Matching method to compute the maximum a posteriori model of an inverse problem where multiple-point statistics, learned from a training image, is used to formulate a closed form expression for an a priori probability density function. This paper discusses aspects of the implementation of the Fre-quency Matching method and the techniques adopted to make it com-putationally feasible also for large-scale inverse problems. The source code is publicly available at GitHub and this paper also provides an example of how to apply the Frequency Matching method to a linear inverse problem.
Multiple-points statistics; Training image; A priori in- formation; Maximum a posteriori model