eCite Digital Repository
Mapping geology and volcanic-hosted massive sulfide alteration in the Hellyer–Mt Charter region, Tasmania, using Random ForestsTM and Self-Organising Maps
Citation
Cracknell, MJ and Reading, AM and McNeill, AW, Mapping geology and volcanic-hosted massive sulfide alteration in the Hellyer-Mt Charter region, Tasmania, using Random ForestsTM and Self-Organising Maps, Australian Journal of Earth Sciences, 61, (2) pp. 287-304. ISSN 0812-0099 (2014) [Refereed Article]
Copyright Statement
Copyright 2013 Geological Society of Australia
DOI: doi:10.1080/08120099.2014.858081
Abstract
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.
Item Details
Item Type: | Refereed Article |
---|---|
Keywords: | random forests, self-organising maps, machine learning, geological mapping, volcanic-hosted massive sulfide, Tasmania |
Research Division: | Earth Sciences |
Research Group: | Geophysics |
Research Field: | Geophysics not elsewhere classified |
Objective Division: | Expanding Knowledge |
Objective Group: | Expanding knowledge |
Objective Field: | Expanding knowledge in the earth sciences |
UTAS Author: | Cracknell, MJ (Dr Matthew Cracknell) |
UTAS Author: | Reading, AM (Professor Anya Reading) |
ID Code: | 92289 |
Year Published: | 2014 |
Funding Support: | Australian Research Council (CE0561595) |
Web of Science® Times Cited: | 42 |
Deposited By: | Earth Sciences |
Deposited On: | 2014-06-12 |
Last Modified: | 2017-10-25 |
Downloads: | 1 View Download Statistics |
Repository Staff Only: item control page