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Unsupervised clustering of continental-scale geophysical and geochemical data using Self-Organising Maps
conference contribution
posted on 2023-05-23, 17:48 authored by Matthew CracknellMatthew Cracknell, Anya ReadingAnya ReadingSelf-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.
Funding
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
History
Publication title
Proceedings of the 3rd Australian Regolith Geoscientists Association ConferenceEditors
VNL WongPagination
20-24Department/School
School of Natural SciencesPublisher
Australian Regolith Geoscientists AssociationPlace of publication
AustraliaEvent title
3rd Australian Regolith Geoscientists Association ConferenceEvent Venue
Bunbury, AustraliaDate of Event (Start Date)
2014-02-06Date of Event (End Date)
2014-02-07Repository Status
- Restricted