Mitigating uncertainties in mineral exploration targeting: Majority voting and confidence index approaches in the context of an exploration information system (EIS)
Yousefi, Mahyar, Lindsay, Mark D., and Kreuzer, Oliver (2024) Mitigating uncertainties in mineral exploration targeting: Majority voting and confidence index approaches in the context of an exploration information system (EIS). Ore Geology Reviews, 165. 105930.
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Abstract
Various mineral prospectivity modelling (MPM) approaches are available for targeting mineral deposits, each method capable of predicting areas of high prospectivity. Given the diversity of MPM approaches, the modelled areas of high prospectivity can differ across different MPMs. However, rather than a negative, different MPM outputs can benefit mineral exploration targeting because each method has its advantages. Rather, the problem lies in the lack of consensus over how to best select and delimit mineral exploration targets from different MPM results. Here we aim to address the challenges outlined above whilst quantifying and mitigating the effects of inherent uncertainties. We first generate eleven different prospectivity models utilising deep learning, machine learning, fuzzy logic, and geometric average integration methods. Then, we adopt a majority voting ensemble technique to incorporate and combine the predictions of each prospectivity model. Next, we propose a confidence index designed to mitigate uncertainty associated with our multi-technique approach to MPM. The confidence index quantifies variation in prospectivity values for each cell of the MPM target area. The conjunction of a confidence index and majority voting model facilitates consistent and robust algorithm-driven extraction of exploration targets based on an ensemble of prospectivity models.
Item ID: | 82015 |
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Item Type: | Article (Research - C1) |
ISSN: | 0169-1368 |
Keywords: | Confidence index, Exploration targeting, Majority voting, Mineral prospectivity modelling (MPM), Uncertainty |
Copyright Information: | © 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Date Deposited: | 20 Aug 2024 04:39 |
FoR Codes: | 37 EARTH SCIENCES > 3705 Geology > 370508 Resource geoscience @ 100% |
SEO Codes: | 25 MINERAL RESOURCES (EXCL. ENERGY RESOURCES) > 2503 Mineral exploration > 250399 Mineral exploration not elsewhere classified @ 100% |
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