An Efficient Sample Selection Methodology for a Geometallurgy Study Utilizing Statistical Analysis Techniques

Abstract

A geometallurgy study aims to link metallurgy and geology to reduce technical risk and enhance the economic performance of a mineral-processing plant. It does so by accounting for variability in a deposit to develop cash flow models with variable throughput rates. High-quality sample selection for metallurgical test work that are representative of the deposit is an essential component of a geometallurgy study, but the large multi-dimensional dataset makes sample selection a daunting task, as classifying the dataset while respecting its heterogeneity is difficult. This paper presents a streamlined approach for sample selection, utilizing statistical analysis techniques in Python. It cuts down time to select samples from around 1200 s per drillhole to about 60 s per drillhole for data classification and from 12 h to 8 h for handpicking samples from the classified dataset, translating to cost savings. The cumulative sum method and k-means clustering method are used in the methodology to elegantly classify the data and select representative samples. The effectiveness of the methodology is demonstrated by presenting data from a pre-feasibility study of a copper-iron mine in which 40 samples were selected for flotation test work.