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As high-grade, mineralogically simple resources are depleted, newly explored deposits are increasingly geologically and mineralogically complex, requiring new approaches to integrate mineralogical, chemical, and textural information into predictive geometallurgical models. At the Nautanen North IOCG deposit, Northern Sweden, a geometallurgical program is being developed following the framework of Michaux and O’Connor (2020), designed to systematize orebody knowledge into mining workflows. Within this research, the program combines drill core imagery, mineral chemistry data and a process mineralogy study to improve early-stage characterization. Within this workflow, the Mineral Co-Occurrence Probability Fields (MCOPF) algorithm (Merrill-Cifuentes et al., 2022), originally developed to work with hyperspectral images, has been adapted for RGB drill core images (Figure 1), enabling the unsupervised clustering of textures into groups that can be systematically evaluated for geochemical and mineralogical variability.

The clustering framework enables the identification of domains that are not only texturally distinct but also expected to differ in metallurgical response (Figure 2). To capture the mineralogical variability, the chemical composition of both ore and gangue minerals is being analysed. These results will support the development of a quantitative mineralogical model that links textural clusters with bulk chemical trends and mineralogical attributes. 
The next stage involves targeted process mineralogy studies to evaluate how the defined textural–mineralogical groups behave at the micro-scale, focusing on grain size, associations, and liberation characteristics. By combining image-based textural clustering, mineral chemistry variability, and micro-scale mineralogical analysis, this methodology establishes a new workflow for geometallurgical characterization of complexly deformed and metamorphosed Paleoproterozoic mineral systems.
The Nautanen North project provides a unique opportunity, as the deposit is at a feasibility stage where geometallurgical modelling can be incorporated before extraction begins. The ultimate objective is to deliver an innovative approach to early-stage ore characterization that enables more robust metallurgical predictions and informs both immediate mine planning and long-term, large-scale decision-making about resource utilization and processing strategies.
This methodology contributes both to the fundamental understanding of IOCG deposits and to practical advances in orebody modelling, by linking textures, mineral chemistry, and processing behaviour into a single predictive framework

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