Optimising the placement of additional drill holes to enhanced mineral resource classification: a case study on a porphyry copper deposit

ABSTRACT

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.

KEYWORDS:

• Mineral resource classification
• kriging variance
• combined variance
• Particle swarm optimisation

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/17480930.2024.2364131.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was funded by the ANID program: Startup Ciencia 2021, SUC 210030; The first author also acknowledges the funding of ANID through grants Fondecyt N° 11220464.