eCite Digital Repository

Predicting Cu species classification using geochemistry at the Productora Cu-Au-Mo Deposit, Chile


Escolme, A and Berry, R and Hunt, J, Predicting Cu species classification using geochemistry at the Productora Cu-Au-Mo Deposit, Chile, Proceedings of the Third AusIMM International Geometallurgy Conference (GeoMet), 15-16 June, Perth, WA, pp. 113-117. (2016) [Refereed Conference Paper]

PDF (Extended abstract)
Restricted - Request a copy

Copyright Statement

Copyright unknown

Official URL:


Predictive geometallurgical models are being developed for the Productora Cu-Au-Mo deposit based on geochemistry to reflect variability in Cu species based on Cu sequential leach data (ie oxide, transitional-oxide, transitional-sulfide, sulfide and non-recoverable Cu).

Productora, located in the coastal range of northern Chile, is a magmatic-hydrothermal tourmaline breccia-hosted, structurally controlled Cu-Au-Mo deposit hosted by a thick sequence of Jurassic rhyodacitic volcanic rocks. North and north-west striking fault sets control the distribution of mineralised breccias. Alteration is widespread, complex and pervasive. Hypogene alteration assemblages range in composition from distal magnetite-amphibole, to sodic-calcic, to sodic, to phyllic, to proximal potassic. Low-temperature advanced argillic assemblages are locally juxtaposed next to proximal high-temperature K-feldspar-tourmaline assemblages. The dominant hypogene Cu phase is chalcopyrite.

A simple Cu species classification scheme based on sequential leach data and S per cent was devised to account for non-recoverable Cu (ie non-sulfide Cu), which is insoluble in weak acids. Through machine learning, a proxy for this Cu species classification scheme was developed based on: depth, Ca per cent, Cu per cent, Fe per cent, K per cent, Mn ppm, S per cent and Ln(Cu/S) plus logged regolith class, thus allowing classification to be extended to areas of the deposit where no sequential leach data are available. The modelled data has been used to generate deposit-wide 3D models for Cu species classification.

The validity of the proxy Cu species classification model is being tested against the results of flotation analysis. Initial results indicate that geochemical proxies can be used to successfully predict Cu species class and provide a high density of classification data.

Item Details

Item Type:Refereed Conference Paper
Keywords:machine learning, copper speciation, oxide copper
Research Division:Earth Sciences
Research Group:Geology
Research Field:Resource geoscience
Objective Division:Mineral Resources (Excl. Energy Resources)
Objective Group:Primary mining and extraction of minerals
Objective Field:Mining and extraction of copper ores
UTAS Author:Escolme, A (Dr Angela Escolme)
UTAS Author:Berry, R (Associate Professor Ron Berry)
UTAS Author:Hunt, J (Dr Julie Hunt)
ID Code:116419
Year Published:2016
Deposited By:CODES ARC
Deposited On:2017-05-09
Last Modified:2017-11-10

Repository Staff Only: item control page