Supervised and unsupervised classification of near-mine soil Geochemistry and Geophysics data

Matthew J. Cracknell; Anya M. Reading; Andrew W. McNeill

doi:10.1071/ASEG2013ab204

ISSN: 2202-0586
E-ISSN:

oa Supervised and unsupervised classification of near-mine soil Geochemistry and Geophysics data
Authors Matthew J. Cracknell^a, Anya M. Reading^b and Andrew W. McNeill^c
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^a ARC Centre of Excellence in Ore Deposits (CODES), University of Tasmania, Private Bag 126, Hobart, , Tasmania, , 7001, [email protected] ^b School of Earth Sciences, University of Tasmania, Private Bag 79, Hobart, , Tasmania, , 7001, [email protected] ^c Mineral Resources Tasmania, Dept. Infrastructure, Energy & Resources, PO Box 56, Rosny Park, , Tasmania, , 7018, [email protected]
Publisher: Australian Society of Exploration Geophysicists
Source: ASEG Extended Abstracts, Volume 2013, Issue ASEG2013 - 23rd Geophysical Conference, Dec 2013, p. 1 - 4
DOI: https://doi.org/10.1071/ASEG2013ab204
- Published online: 01 Dec 2013

Abstract

Remotely sensed geoscience data can assist detailed geological field mapping in areas of thick vegetation and poor outcrop. However, the potentially high dimensionality of these data makes it difficult to visually interpret and fully comprehend. Machine learning algorithms provide an efficient semi-automated means of recognising and identifying patterns in data. We use Random Forests for supervised classification of geologic units from airborne geophysical and soil geochemical data in the economically significant Hellyer - Mt Charter region of western Tasmania. A backward-recursive variable selection method is used to select the most relevant and useful data for this problem. This reduces computation cost and enhances interpretation of results without significantly affecting prediction accuracy. Random Forests generates accurate predictions of the spatial distribution of surface geologic units from these data. An example is provided regarding the use of Self- Organising Maps, an unsupervised clustering algorithm, to identify distinct but spatially contiguous clusters within a geologic unit. By visualising cluster spatial distribution and identifying key variable contributions to cluster differences, we interpret the geological significance of intra-class variability.

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2026-01-20

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References

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/content/journals/10.1071/ASEG2013ab204

Article Type: Research Article

Keyword(s): Geophysics Geochemistry; Supervised classification; Tasmania; Unsupervised clustering

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oa Supervised and unsupervised classification of near-mine soil Geochemistry and Geophysics data

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