Tom Sheldrake , Oliver John Higgins

Minerals exhibit zoning patterns that can be related to changes in the environment in which they grew. Using statistical methods that have been designed to segment optical images, we have developed a procedure to segment zonation within minerals and correlate these zones between multiple crystals using elemental maps. This allows us to quantify the complexity and variability of chemical zoning between different geological samples. Specifically, we employ a simple linear iterative clustering algorithm, which splits the chemical maps into spatially constrained regions of similar chemistry. The result is a texturally segmented crystal, akin to what would be identified by the human eye. To aid the segmentation and correlation of zones, we also introduce a new method to classify multiple mineral phases within a single thin section. This is based on a finite mixture model approach, which proves very effective in removing mixed pixels that will only introduce noise into the segmentation. We provide an example using the mineral phase plagioclase. Using two contemporaneous samples from an eruptive unit on the island of St. Kitts we show that a volcanic bomb (~10cm) and scoria (~2cm) have similar rim compositions but distinctly different core compositions. Our methodology will enable a statistical characterization of 2D complexity of crystals in a variety of different geo-scientific disciplines. This will allow the genesis of different mineral phases to be characterised and directly compared.