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

Citation

Cracknell, MJ and Reading, AM and McNeill, AW, Supervised and unsupervised classification of near-mine soil Geochemistry and Geophysics data, Proceedings of the 23rd International Geophysical Conference and Exhibition, 11-14 August 2013, Melbourne, Australia, pp. 1-4. (2013) [Refereed Conference Paper]


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Copyright Statement

copyright 2013 CSIRO Publishing

Official URL: http://dx.doi.org/10.1071/ASEG2013ab204

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.

Item Details

Item Type:Refereed Conference Paper
Keywords:supervised classification, unsupervised clustering, geophysics geochemistry, Tasmania
Research Division:Earth Sciences
Research Group:Geophysics
Research Field:Geophysics not elsewhere classified
Objective Division:Mineral Resources (excl. Energy Resources)
Objective Group:Mineral Exploration
Objective Field:Mineral Exploration not elsewhere classified
Author:Cracknell, MJ (Dr Matthew Cracknell)
Author:Reading, AM (Professor Anya Reading)
ID Code:86778
Year Published:2013
Deposited By:Earth Sciences
Deposited On:2013-10-21
Last Modified:2017-11-03
Downloads:208 View Download Statistics

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