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<p>The growing demand for strategic and critical minerals requires mineral prospectivity mapping (MPM) frameworks that are reproducible and uncertainty-aware. Yet, adoption in national policy and industry is limited by opaque methodology, unquantified or only partially quantified uncertainty, and other outstanding problems in MPM. We, therefore, further developed an ensemble-learning method for MPM that explicitly propagates and analyses multi-source uncertainty, and applied it to predict the <i>prospectivity</i> for Fe–Mn mineralization across South Africa. Our deep and large ensemble of 1040 models is evaluated against new certified reference targets (i.e., Sishen–Postmasburg Iron and Manganese Field; Kalahari Manganese Field). Our prospectivity maps and complementary uncertainty surfaces enable simultaneous appraisal of favorability and confidence and are a proxy for investment risk. The maps reproduce known mineralized provinces and delineate new, previously overlooked targets along crustal-scale structures, notably the Thabazimbi–Murchison Lineament. Robust uncertainty analysis uses six metrics to identify zones of model disagreement. Coupling reproducible and open methodology, and feed-forward validation using certified reference targets, we provide an audit-ready recast of MPM into a decision-support instrument for national exploration policy, mineral actuarial science, land-use planning, and environmental risk assessment, with broader implications for mineral corridors and critical raw material strategies.</p> Graphical Abstract <p></p>

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Robust Uncertainty Analysis in Mineral Prospectivity Mapping: A Prototype for Fe–Mn Exploration in South Africa

  • 期刊论文

  • 发表日期：

    2025年12月24日

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  • Glen T. Nwaila,
  • Raymond J. Durrheim,
  • Hartwig E. Frimmel,
  • Emmanuel John M. Carranza

摘要

The growing demand for strategic and critical minerals requires mineral prospectivity mapping (MPM) frameworks that are reproducible and uncertainty-aware. Yet, adoption in national policy and industry is limited by opaque methodology, unquantified or only partially quantified uncertainty, and other outstanding problems in MPM. We, therefore, further developed an ensemble-learning method for MPM that explicitly propagates and analyses multi-source uncertainty, and applied it to predict the prospectivity for Fe–Mn mineralization across South Africa. Our deep and large ensemble of 1040 models is evaluated against new certified reference targets (i.e., Sishen–Postmasburg Iron and Manganese Field; Kalahari Manganese Field). Our prospectivity maps and complementary uncertainty surfaces enable simultaneous appraisal of favorability and confidence and are a proxy for investment risk. The maps reproduce known mineralized provinces and delineate new, previously overlooked targets along crustal-scale structures, notably the Thabazimbi–Murchison Lineament. Robust uncertainty analysis uses six metrics to identify zones of model disagreement. Coupling reproducible and open methodology, and feed-forward validation using certified reference targets, we provide an audit-ready recast of MPM into a decision-support instrument for national exploration policy, mineral actuarial science, land-use planning, and environmental risk assessment, with broader implications for mineral corridors and critical raw material strategies.

Graphical Abstract