Summary:
Mineral resources play a vital role in the global economy, and their accurate classification is crucial for effective exploration, mining, and management within a mining project. A common goal in mineral resource classification is to reduce the uncertainty in the estimated deposit model. Drilling additional holes to gather more information is an intuitive way to reduce uncertainty. However, drilling is costly, and determining the optimal locations for new drill holes is often a subject of debate. In this study, the optimal locations for five additional drill holes are determined using three different cost functions based on geostatistical parameters, combined with the particle swarm optimization (PSO) algorithm. The results demonstrate that selecting drill hole locations through the optimization of a cost function can effectively reduce uncertainty and improve mineral resource classification.
CV:
Mohammad Malek is an associate professor in the Department of Metallurgical and Mining Engineering at Universidad Católica del Norte. He holds a Bachelor’s degree in Mining Engineering (2009) and a Master’s degree in Mining (2011) from the Polytechnic of Tehran, Iran. He completed his Ph.D. in Mining Engineering at the University of Chile in 2016 and later conducted postdoctoral research at the Advanced Mining Technology Center (AMTC). His areas of expertise include geostatistical modeling, orebody evaluation, and geological and geometallurgical modeling. He has published more than 30 papers on these topics in indexed journals (Web of Science).
mohammad.maleki@ucn.cl
