TY - JOUR
T1 - Mitigating uncertainties in mineral exploration targeting
T2 - Majority voting and confidence index approaches in the context of an exploration information system (EIS)
AU - Yousefi, Mahyar
AU - Lindsay, Mark D.
AU - Kreuzer, Oliver
N1 - Funding Information:
The first author thanks National Iranian Copper Industries Company for that working with the company gives him the ideas of problem definition and solving. He thanks Dr. Mohammad Rezaie for some discussions about majority voting. The authors express their sincere gratitude to the reviewers and editors for that their comments helped them to improve this paper. Mark D. Lindsay acknowledges support from the Australian Research Council, Australia, Industrial Transformational Training Centre Data Analytics for Resource and Environment IC190100031.
Funding Information:
The first author thanks National Iranian Copper Industries Company for that working with the company gives him the ideas of problem definition and solving. He thanks Dr. Mohammad Rezaie for some discussions about majority voting. The authors express their sincere gratitude to the reviewers and editors for that their comments helped them to improve this paper. Mark D. Lindsay acknowledges support from the Australian Research Council, Australia, Industrial Transformational Training Centre Data Analytics for Resource and Environment IC190100031 .
Publisher Copyright:
© 2024 The Author(s)
PY - 2024/2
Y1 - 2024/2
N2 - 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.
AB - 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.
KW - Confidence index
KW - Exploration targeting
KW - Majority voting
KW - Mineral prospectivity modelling (MPM)
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85183977071&partnerID=8YFLogxK
U2 - 10.1016/j.oregeorev.2024.105930
DO - 10.1016/j.oregeorev.2024.105930
M3 - Article
AN - SCOPUS:85183977071
SN - 0169-1368
VL - 165
JO - Ore Geology Reviews
JF - Ore Geology Reviews
M1 - 105930
ER -