Chlorite geochemical vectoring of ore bodies: a natural kind clustering approach

Abstract

Chlorite has long been considered a mineral group likely to have different trace element chemistry with proximity to mineralization, and therefore can be used to vector towards ore bodies. However, due to their geochemical complexity, it has proven challenging to develop a simple vectoring method based on the variation in abundance of one or a few chemical elements or isotopes. Machine learning, specifically cluster analysis, provides a potential mathematical tool for characterizing multidimensional geochemical correlations with proximity to mineralization. In this contribution we conducted a cluster analysis on 23 elements from 1,679 distinct chlorite sample analyses. The combination of this clustering technique with classification by proximity to the ore body, 1) explores and characterizes the nature of chlorite composition and proximity to ore bodies and 2) tests the efficacy of clustering-classification methods to predict whether a chlorite sample is near to an ore body. We found that chlorite chemistry is more strongly controlled by deposit type than proximity to mineralization and that cluster analysis of chlorite trace element content is likely not a viable way to develop vectors towards porphyry mineralization.