Data-driven delineation of geometallurgical domains in porphyry copper systems
DOI:
https://doi.org/10.17159/Abstract
Partitioning an orebody into spatially coherent domains supports stationarity and improves the reliability of resource estimates and metallurgical forecasts. Conventional domaining commonly relies on lithological or grade interpretations and can overlook subtler, yet economically important, variations in mineralogical, geochemical, and metallurgical attributes that affect recovery predictions. Unsupervised clustering offers a more data-driven means of defining such domains. This study presents a machine learning workflow applied to a porphyry copper dataset comprising geochemical and processing variables. The variables were standardised and weighted using Bayesian optimisation to improve domain definition. Weighted dissimilarities were then represented in a lower-dimensional space using Multidimensional Scaling and clustered with the K-means algorithm to generate candidate domains. Validity indices guided domain selection, while Principal Component Analysis within each domain assessed internal consistency and dominant compositional patterns. Cluster assignments were then mapped back into drillhole space and interpreted against established porphyry copper alteration zonation. The resulting domains show reduced internal variability while remaining geologically plausible and broadly consistent with known alteration patterns. Accurate delineation of such domains can improve geometallurgical modelling and operational planning because more consistent processing behaviour provides a clearer basis for anticipating milling response and recovery variability across the orebody. This reproducible workflow links orebody heterogeneity to processing behaviour and provides a practical, data-driven framework for domaining in complex deposits.
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