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Mineral Reconnaissance Through Scientific Consensus: First National Prospectivity Maps for PGE–Ni–Cu–Cr and Witwatersrand-type Au Deposits in South Africa

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journal contribution
posted on 2024-12-04, 12:10 authored by Glen T. Nwaila, Steven E. Zhang, Julie E. Bourdeau, Emmanuel John M. Carranza, Stephanie Enslin, Musa S. D. Manzi, Fenitra Andriampenomanana, Yousef GhorbaniYousef Ghorbani

 

This paper presents a pioneering approach to mineral prospectivity mapping (MPM) in South Africa by introducing a novel, consensus-based method for identifying potential deposits of Platinum Group Elements (PGE), Nickel (Ni), Copper (Cu), Chromium (Cr), and Witwatersrand-type gold (Au). The research outlines the development and validation of this method, which leverages scientific consensus through deep ensemble modeling. By simulating the decision-making process of multiple data scientists, the method captures uncertainties in workflow choices, producing more reliable and confident MPM outputs.

The validation of this method was conducted using well-known, resource-rich geological systems—the Bushveld Complex for PGE-Ni-Cu-Cr and the Witwatersrand Basin for Au. These regions serve as benchmark sites due to their extensive geological knowledge and the presence of mega-deposits, providing a robust test of the method’s effectiveness. The results demonstrate high agreement with existing geological and exploration data, identifying high-potential exploration targets in both known and new areas. For instance, new targets for PGE-Ni-Cu-Cr were identified northwest of the Bushveld Complex, and promising gold exploration areas were pinpointed west of the Witwatersrand Basin.

The broader impact of this research lies in its ability to increase trust in data-driven exploration tools within the mineral industry by offering a method that not only highlights prospective areas but also quantifies the confidence level in these predictions. This approach is particularly valuable as the industry increasingly relies on data-driven techniques for exploration, ensuring that decision-makers can base their strategies on scientifically validated and consensus-driven information.

History

School affiliated with

  • College of Health and Science (Research Outputs)
  • School of Natural Sciences (Research Outputs)

Publication Title

Natural Resources Research

Volume

33

Issue

6

Pages/Article Number

2357–2384

Publisher

Springer

ISSN

1520-7439

eISSN

1573-8981

Date Submitted

2024-05-29

Date Accepted

2024-07-17

Date of First Publication

2024-08-14

Date of Final Publication

2024-12-01

Relevant SDGs

  • SDG 9 - Industry, Innovation and Infrastructure
  • SDG 12 - Responsible Consumption and Production

Open Access Status

  • Open Access

Date Document First Uploaded

2024-08-18