Article ID Journal Published Year Pages File Type
5752654 Applied Geochemistry 2016 11 Pages PDF
Abstract

•We evaluate a clustering procedure by applying it to geochemical data.•The procedure generates a hierarchy of clusters.•Different levels of the hierarchy show geochemical processes at different spatial scales.•The clustering method is Bayesian finite mixture modeling.•Model parameters are estimated with Hamiltonian Monte Carlo sampling.

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.

Related Topics
Physical Sciences and Engineering Earth and Planetary Sciences Geochemistry and Petrology
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