Using Clustered Heat Maps in Mineral Exploration to Visualize Volcanic-Hosted Massive Sulfide Alteration and Mineralization

Abstract

This study proposes an extension of a visualization approach common in biochemistry (the clustered heat maps—CHMs) to geochemical data with the main objective of detecting hydrothermal alteration and mineralization. The approach allows superior visualization of unsupervised cluster analysis results. We consider two examples: a synthetic case study and an application to public data derived from the Canadian Flin Flon volcanic-hosted massive sulfide deposits. A series of experiments were run on a synthetic dataset with the aim of understanding the effect of noise and how random data sampling of variable specimen population size influences results of a variety of clustering algorithms (including K-means and other hierarchical methods) and their visualization using CHMs. These experiments on synthetic data provided the basis to propose a possible workflow for the selection of optimal classifiers to be applied on natural data and the definition of an appropriate parametrization (distance metrics and clustering algorithm). Natural data analysis provides direct evidence of how CHMs can be a fruitful approach in mineral exploration if compared to other cluster analysis methods (e.g., classic K-means or hierarchical methods), CHMs provide the opportunity of examining an additional dimension of clustering and still view chemical compositions (although in a transformed space) in a single plot. Facilitated selection of appropriate levels of granularity (G), which regulates the scale of clustering in a CHM, was found to be an instrumental tool and led to the successful separation of clusters representative of major lithological transitions vs. smaller clusters, at higher granularity, isolating VHMS alteration and mineralization. Integration of statistical tests conducted on synthetic data, together with CHM’s visualization of the classification results led us to consider the Manhattan–Ward classifier as an optimal pair for the Flin Flon dataset, despite its limitations induced by the ‘uniform effect.’