Lithological discrimination based on statistical analysis of multi-sensor drill core logging data in the Matagami VMS district, Quebec, Canada

Abstract

In mineral exploration, traditional core logging is largely based on the visual inspection of drilled cores, a process which is often subjective and non-reproducible. However, a number of physical, chemical and mineralogical properties of rocks can now be measured at high spatial resolution on drill cores. The resulting large multiparameter databases can help geologists to quantitatively discriminate between lithologies, study hydrothermal alteration, and potentially vector towards mineralization. Multivariate statistical methods are important tools to assist geologists in interpreting such large databases. We present an application of model-based cluster analysis in the Matagami base metal mining district, more specifically to improve lithological discrimination at the zinc-rich Bracemac and McLeod volcanogenic massive sulfide (VMS) deposits. The model-based cluster analysis method is able to efficiently discriminate different geological units encountered, even in cases where two units are visually similar or in the presence of strong hydrothermal alteration.