Technical Note: Cluster Analysis of Inverse Thermochronology Models

Abstract. Thermochronological inverse modeling may produce none-unique solution, that can group different thermal histories. Objective identification of such groups, also referred as "path families", is challenging and greatly benefits from dimension-reducing exploratory data analysis tools. This article proposes a statistical algorithm to overcome these challenges. We show that Hausdorff and Frechet distances are viable dissimilarity measures for ordered point sets, such as time-temperature paths. Clustering the pairwise dissimilarities between modeled thermal histories reveals distinct groups of thermal histories for a given sample or set of samples. As demonstrated by clustering a natural example, automated path-clustering allows for an objective and reproducible interpretation and maybe particularly useful for samples with poor prior knowledge of the time-temperature history. To allow adoption of the method by the thermochronology community, the methods introduced in this article are freely available through the package software thermoclustr, written in the programming language R.