No abstract

Dimensionality reduction provides a simple, two-dimensional representation of multi-element geochemical assays, which facilitates visualisation of complex data and enhances their interpretation.A recently proposed dimensionality reduction algorithm, namely t-distributed stochastic neighbour embedding (t-SNE), generates effective two-dimensional representations of a wide range of datasets based on pairwise statistical distances of the input. However, direct application to multi-element geochemical assays has been shown to produce representations which can fail to separate specimens by a desired geological property, such as state of hydration. Since t-SNE is a statistical distance-based method, these sub-optimal representations may be due to the presence of dimensions (i.e., elements) irrelevant to the desired property-an issue often termed the 'curse of dimensionality'. To address this shortcoming, t-SNE was applied to (i) 31 elements in a geochemical assay database covering 16 000 drill core intervals intersecting the Kevitsa mafic-ultramafic intrusion (Lapland, Finland); and (ii) a subset of 11 elements capable of discriminating between unaltered and altered host rock specimens, as determined by a Random Forest classifier within a recursive feature elimination framework. The resulting representation more effectively separates altered and unaltered specimens, and we demonstrate that it produces more favourable representations than alternative well-known methods (namely, a self-organising map and principal components analysis) applied to the same dataset. We also demonstrate that the proposed t-SNE representation is applicable for re-logging of the specimens' alteration state as logged by geologists, and in particular provides visual insight into the labels suggested by a black box statistical re-logging algorithm.1 Mr. Tom Horrocks was responsible for experimental design, evaluation, and writing the manuscript. Prof. Eunjung Holden and Dr. Daniel Wedge critically reviewed the manuscript with focus on computational elements. Dr. Chris Wijns provided details regarding the case study (Kevitsa). Both Dr. Chris Wijns and Dr. Marco Fiorentini critically reviewed manuscript with focus on geoechemical interpretation.

No abstract

No abstract