SEM-based automated mineralogy (SEM-AM) and unsupervised machine learning studying the textural setting and elemental association of gold in the Rajapalot Au-Co area, northern Finland

Abstract

SEM-based automated mineralogy (SEM-AM) techniques allow fast and effective way of studying the textural settings of gold in hydrothermal deposits. Unsupervised machine learning (e.g. self-organizing maps) is an intuitive way of processing multi-dimensional geochemical datasets in order to reveal hidden patterns potentially represent different mineralization stages. We combined these two methods for studying the relationship of gold and cobalt within different prospects in a Paleoproterozoic gold-cobalt mineralized area known as Rajapalot. Gold is found as a texturally late phase, occurring in fractures of silicates and sulfides. Based on the elemental associations observed from the whole-rock geochemical dataset using self-organizing-maps, Co-only, Au-Co and Au associations can be inferred relating to either different mineralization stages or different fluid-host rock interactions. Also, the dominant mineralization-related alteration in different occurrences within the Rajapalot Au-Co prospects are reflected as elemental associations with gold in the geochemical data. Our study shows the effectiveness SEM-AM methods for studying distribution of valuable minerals. Unsupervised neural networks provide for easy and intuitive processing technique that can be validated with the mineralogical observations.