Modelling mineralogy from whole rock assay data – a case study from Productora Cu-Au-Mo deposit, Chile
Escolme, A and Berry, R and Hunt, J and Halley, S and Potma, W, Modelling mineralogy from whole rock assay data - a case study from Productora Cu-Au-Mo deposit, Chile, Resources for future generations, 16-21 June 2018, Vancouver, Canada, pp. 1. (2018) [Conference Extract]
Mineralogy is a fundamental characteristic for a given rock mass throughout the mining value chain. Understanding the
mineralogy of the bulk rock, including both the gangue and ore components, is critical when predicting processing
behavior and waste characteristics. Throughout the exploration and resource development process, mineralogical data
are collected mostly in a qualitative manner through visual logging. These datasets are subjective and commonly
inconsistent. Current methods for quantitative estimates of bulk mineralogy (e.g. X-ray point counting using SEM-EDS
based software packages, and QXRD) are expensive and often very slow.
We present two new approaches to predicting bulk mineralogy using commonly available 33-element geochemical
assay data. Firstly, we demonstrate qualitative estimation from assay using geochemical discrimination plots and
validate this approach using quantitative XRD data. Secondly, we demonstrate quantitative estimation by calculated
mineralogy. These approaches are presented using the Productora Cu-Au-Mo deposit, Chile, as a case study.
Productora provides an example of an early stage project where geometallurgical models of mineralogy have been used
to mitigate risk and uncertainty.
Our results indicate that robust, deposit-wide, predictions of bulk mineralogy can be generated quickly and cost
effectively from geochemical assay data. These methods provide semi-quantitative mineralogical data for every
multi-element geochemistry sample interval across the whole deposit. The number and spatial distribution of
mineralogical data points can be significantly increased, by orders of magnitude. This data is then available to construct
geometallurgical models and provides truly representative, deposit wide, mineralogical variability data. In turn, this
provides significant benefits for the selection of representative samples for metallurgical test work, or the siting of
specific metallurgical test work holes. Quantitative estimates of mineralogy on each assay interval can also be directly
incorporated into resource block models, for mine planning purposes.
calculated mineralogy, geometallurgy, linear programming, Productora, whole rock geochemistry, alteration, mineralogy