Optimising the placement of additional drill holes to enhanced mineral resource classification: a case study on a porphyry copper deposit
Maleki M. Baeza D. Soltani-Mohammadi S. Madani N. Díaz E. Anguita F.
2025Taylor and Francis Ltd.
International Journal of Mining, Reclamation and Environment
2025#39Issue 2134 - 151 pp.
In this study, we compared outcomes of optimising the placement of five additional drill holes using three geostatistical cost functions (AKV, WAKV, and CV) and the Particle Swarm Optimisation algorithm (PSO). WAKV identified locations with higher average copper grades compared to AKV. Conversely, CV suggested sites with high kriging variance and copper grade variation. Initial holes, alongside those determined by each cost function, were used to classify mineral resources. Findings underscored the effectiveness of optimising drill hole placement based on cost functions in reducing uncertainty and improving mineral resource classification.
combined variance , kriging variance , Mineral resource classification , Particle swarm optimisation
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Department of Metallurgical and Mining Engineering, Universidad Católica del Norte, Antofagasta, Chile
Department of Electrical Engineering, Faculty of Physical and Mathematical Sciences, University of Chile, Santiago, Chile
Department of Mining Engineering, University of Kashan, Kashan, Iran
School of Mining and Geosciences, Nazarbayev University, Astana, Kazakhstan
Lidenbrock, Gea Spa, Santiago, Chile
Department of Metallurgical and Mining Engineering
Department of Electrical Engineering
Department of Mining Engineering
School of Mining and Geosciences
Lidenbrock
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