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Unsupervised clustering of continental-scale geophysical and geochemical data using Self-Organising Maps

Citation

Cracknell, MJ and Reading, AM, Unsupervised clustering of continental-scale geophysical and geochemical data using Self-Organising Maps, Proceedings of the 3rd Australian Regolith Geoscientists Association Conference, 6-7 February 2014, Bunbury, Australia, pp. 20-24. (2014) [Non Refereed Conference Paper]


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Abstract

Self-Organising Maps is a data-driven approach for exploring and analysing disparate, high-dimensional data. In this experiment Self-Organising Maps is used to cluster remotely sensed geophysical and geochemical data covering the Australian continent into geologically meaningful groups. Our analysis of SOM derived clusters indicates the Australian continent can be represented by five generalised geochronological domains. These geochronological domains contain a number of lithologies symbolising bedrock and regolith units with distinct characteristics.

Item Details

Item Type:Non Refereed Conference Paper
Keywords:self-organising maps, geophysics, geochemistry, machine learning, regolith
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
Author:Cracknell, MJ (Dr Matthew Cracknell)
Author:Reading, AM (Professor Anya Reading)
ID Code:92286
Year Published:2014
Funding Support:Australian Research Council (CE0561595)
Deposited By:Earth Sciences
Deposited On:2014-06-12
Last Modified:2014-06-12
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