Testing cluster analysis on combined petrophysical and geochemical data for rock mass classification

Abstract

New drilling, measurement-while-drilling and top-of-hole sensing technologies are being developed to overcome the challenges of exploration for new mineral deposits under deep cover. These methods will provide continuous, near-real time data collection from every drillhole in the future. Consequently, there will be a need for efficient methods of analysing and interpreting this data stream to complement the exploration strategy. We demonstrate the usefulness of cluster analysis for rapid, automated rock mass classification, and the impact of selecting different subsets of the available data on the classification results. Our study shows that only a few measurements are needed to broadly domain the intersected rock mass and highlights the importance of selecting correct input data depending on the purpose of the classification. Our analysis also indicates the potential of identifying textural and rock mechanical properties from petrophysical measurements via cluster analysis.