Manual hierarchical clustering of regional geochemical data using a Bayesian finite mixture model

Applied Geochemistry
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Abstract

Interpretation of regional scale, multivariate geochemical data is aided by a statistical technique called “clustering.” We investigate a particular clustering procedure by applying it to geochemical data collected in the State of Colorado, United States of America. The clustering procedure partitions the field samples for the entire survey area into two clusters. The field samples in each cluster are partitioned again to create two subclusters, and so on. This manual procedure generates a hierarchy of clusters, and the different levels of the hierarchy show geochemical and geological processes occurring at different spatial scales. Although there are many different clustering methods, we use Bayesian finite mixture modeling with two probability distributions, which yields two clusters. The model parameters are estimated with Hamiltonian Monte Carlo sampling of the posterior probability density function, which usually has multiple modes. Each mode has its own set of model parameters; each set is checked to ensure that it is consistent both with the data and with independent geologic knowledge. The set of model parameters that is most consistent with the independent geologic knowledge is selected for detailed interpretation and partitioning of the field samples.

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Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Manual hierarchical clustering of regional geochemical data using a Bayesian finite mixture model
Series title Applied Geochemistry
DOI 10.1016/j.apgeochem.2016.05.016
Volume 75
Year Published 2016
Language English
Publisher Elsevier
Contributing office(s) Crustal Geophysics and Geochemistry Science Center
Description 11 p.
First page 200
Last page 210
Country United States
State Colorado
Online Only (Y/N) N
Additional Online Files (Y/N) N