University of Tasmania

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Mapping geology and volcanic-hosted massive sulfide alteration in the Hellyer–Mt Charter region, Tasmania, using Random ForestsTM and Self-Organising Maps

journal contribution
posted on 2023-05-18, 01:27 authored by Matthew CracknellMatthew Cracknell, Anya ReadingAnya Reading, McNeill, AW
The Hellyer–Mt Charter region of western Tasmania includes three known and economically significant volcanic-hosted massive sulfide (VHMS) deposits. Thick vegetation and poor outcrop present a considerable challenge to ongoing detailed geological field mapping in this area. Numerous geophysical and soil geochemical datasets covering the Hellyer–Mt Charter region have been collected in recent years. These data provide a rich source of geological information that can assist in defining the spatial distribution of lithologies. The integration and analysis of many layers of data in order to derive meaningful geological interpretations is a non-trivial task; however, machine learning algorithms such as Random Forests and Self-Organising Maps offer geologists methods for indentifying patterns in high-dimensional (many layered) data. In this study, we validate an interpreted geological map of the Hellyer–Mt Charter region by employing Random ForestsTM to classify geophysical and geochemical data into 21 discrete lithological units. Our comparison of Random Forests supervised classification predictions to the interpreted geological map highlights the efficacy of this algorithm to map complex geological terranes. Furthermore, Random Forests identifies new geological details regarding the spatial distributions of key lithologies within the economically important Que-Hellyer Volcanics (QHV). We then infer distinct but spatially contiguous sub-classes within footwall and hangingwall, basalts and andesites of the QHV using Self-Organising Maps, an unsupervised clustering algorithm. Insight into compositional variability within volcanic units is gained by visualising the spatial distributions of sub-classes and associated statistical distributions of key geochemical data. Compositional differences in volcanic units are interpreted to reflect contrasting primary composition and VHMS alteration styles. We conclude that combining supervised and unsupervised machinelearning algorithms provides a widely applicable, robust means, of analysing complex and disparate data for machine-assisted geological mapping in challenging terranes.


Australian Research Council

AMIRA International Ltd

ARC C of E Industry Partner $ to be allocated

Anglo American Exploration Philippines Inc

AngloGold Ashanti Australia Limited

Australian National University

BHP Billiton Ltd

Barrick (Australia Pacific) PTY Limited

CSIRO Earth Science & Resource Engineering

Mineral Resources Tasmania

Minerals Council of Australia

Newcrest Mining Limited

Newmont Australia Ltd

Oz Minerals Australia Limited

Rio Tinto Exploration

St Barbara Limited

Teck Cominco Limited

University of Melbourne

University of Queensland

Zinifex Australia Ltd


Publication title

Australian Journal of Earth Sciences








School of Natural Sciences


Taylor & Francis

Place of publication

54 University St, P O Box 378, Carlton, Australia, Victoria, 3053

Rights statement

Copyright 2013 Geological Society of Australia

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  • Restricted

Socio-economic Objectives

Expanding knowledge in the earth sciences

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