@book{279c4fbbbcd14743ab4589b5f5929c1f,

title = "An Implementation of the Frequency Matching Method",

abstract = "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.",

keywords = "Multiple-points statistics, Training image, A priori in- formation, Maximum a posteriori model",

author = "Katrine Lange and Jan Frydendall and Hansen, {Thomas Mejer} and Andrea Zunino and Klaus Mosegaard",

year = "2013",

language = "English",

series = "DTU Compute-Technical Report-2013",

publisher = "Technical University of Denmark",

number = "09",

}