For pre-concentration evaluations, SRK conducts two types of heterogeneity analyses – Composite-Sample Relationship and Heterogeneity and Scale. Heterogeneity analyses were performed on a polymetallic underground operation to assess the potential for pre-concentration as part of a broader economic evaluation. More than half a million sample intervals were analyzed, constrained within geological and mining domains.
Using the Composite-Sample Relationship method, composites were analyzed on 30 m vertical intervals with mine-specific value parameters to classify ore and waste. Heterogeneity measures quantify the proportions of Waste in Ore (below cut-off material within ore composites; see figure) and Ore in Marginal (above cut-off material in sub-economic composites). Thes heterogeneity measures were used to assess potential pre-concentration strategies and served as direct inputs to SRK’s pre-concentration economic models.
Across the operation, Waste in Ore averaged 30–40% within existing stopes, with slightly higher proportions in low-grade zones. Outside the mined stopes, the trend reversed—there was more below-cut-off material within ore but less above-cut-off material in marginal areas. Marginal zones exhibited approximately 30% Ore in Marginal, with the grades of above cutoff material sufficiently high to justify pre-concentration. Overall, mining areas exhibited different trends in heterogeneity measures, possibly leading to different pre-concentration strategies.
The analysis confirmed that significant internal dilution occurs, even within mined stopes, and that meaningful upgrading potential remains both inside and outside current mining areas. These findings demonstrate how heterogeneity measures guide pre-concentration strategy selection—highlighting zones suitable for waste rejection versus those better suited for reserve extension.
The results will feed directly into SRK’s Size the Prize evaluation to estimate the economic potential through preconcentration. Subsequent test work will refine recovery assumptions and integrate revised grade distributions into pit and stope optimizations.
