Multivariate Geochemical Tectonic Discrimination: Practical Approaches, Limitations and Opportunities

Morgan Williams; Jens Klump; Steve Barnes

doi:10.1080/22020586.2019.12073190

2nd Australasian Exploration Geoscience Conference: Data to Discovery

ISSN: 2202-0586
E-ISSN:

oa Multivariate Geochemical Tectonic Discrimination: Practical Approaches, Limitations and Opportunities
Authors Morgan Williams^a, Jens Klump^b and Steve Barnes^c
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^a CSIRO Mineral Resources ARRC, , Kensington, , WA, [email protected] ^b CSIRO Mineral Resources ARRC, , Kensington, , WA, [email protected] ^c CSIRO Mineral Resources ARRC, , Kensington, , WA, [email protected]
Publisher: Australian Society of Exploration Geophysicists
Source: ASEG Extended Abstracts, Volume 2019, Issue 2nd Australasian Exploration Geoscience Conference: Data to Discovery, Dec 2019, p. 1 - 3
DOI: https://doi.org/10.1080/22020586.2019.12073190
- Published online: 01 Dec 2019

Abstract

Summary

Modern machine learning approaches to multivariate geochemical tectonic discrimination overcome limitations of classical graphical methods, and leverage large public geochemical databases to achieve high classification accuracy. Here we document practical approaches to developing classification models using free and open source tools, and investigate methods for visualising classification uncertainties and high-dimensional spatial relationships. In applying these classification methods into deep time, issues of biases and secular change become apparent. We discuss some opportunities to build upon these models and provide richer insight for future classification models.

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References

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Williams, M., 2019, Pyrolite. github.com/morganjwilliams/pyrolite

/content/journals/10.1080/22020586.2019.12073190

Article Type: Research Article

Keyword(s): data; geochemistry; open source; python; uncertainty

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