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A comparison of random forests and cluster analysis to identify ore deposits type using LA-ICPMS analysis of pyrite

Citation

Gregory, DD and Cracknell, MJ and Large, RR and McGoldrick, P and Kuhn, S and Baker, MJ and Fox, N and Belousov, I and Steadman, JA and Fabris, AJ and Maslennikov, VV and Lyons, TW and Figueroa, MC, A comparison of random forests and cluster analysis to identify ore deposits type using LA-ICPMS analysis of pyrite, Proceedings of the 15th SGA Biennial Meeting: Life with Ore Deposits on Earth, 27-30 August 2018, Glasgow, Scotland, pp. 1274-1277. (2019) [Refereed Conference Paper]


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Official URL: https://www.sga2019glasgow.com/

Abstract

As exploration for new resources increasingly relies upon deeper and deeper drilling to investigate through overburden, exploration projects will encounter significantly higher drilling costs to sample target areas and to open new areas to exploration. Therefore, as much information as possible must be extracted from every drill hole. One tool that can be used is the in situ trace element analysis of individual mineral phases using LA-ICPMS. In this study, we investigate the use of pyrite trace element chemistry to fingerprint different ore deposit types so that appropriate geologic models can be employed at an early stage of exploration in new greenfields areas. While this data is effective at identifying ore deposit type, variability within the raw data leads to inherent complications for manual analysis. One way to deconvolute this data is to employ machine learning algorithms to aid in the classification. Here we develop a classifier using supervised classification (Random Forests) and further test non-supervised classification (cluster analysis) algorithms. The results of using Random Forests and cluster analysis to identify ore deposit type are then compared.

Item Details

Item Type:Refereed Conference Paper
Keywords:pyrite, classification, clustering, mineral chemistry
Research Division:Earth Sciences
Research Group:Geochemistry
Research Field:Exploration Geochemistry
Objective Division:Mineral Resources (excl. Energy Resources)
Objective Group:Other Mineral Resources (excl. Energy Resources)
Objective Field:Mineral Resources (excl. Energy Resources) not elsewhere classified
UTAS Author:Cracknell, MJ (Dr Matthew Cracknell)
UTAS Author:Large, RR (Professor Ross Large)
UTAS Author:McGoldrick, P (Dr Peter McGoldrick)
UTAS Author:Kuhn, S (Mr Stephen Kuhn)
UTAS Author:Baker, MJ (Dr Michael Baker)
UTAS Author:Fox, N (Dr Nathan Fox)
UTAS Author:Belousov, I (Dr Ivan Belousov)
UTAS Author:Steadman, JA (Dr Jeffrey Steadman)
ID Code:135338
Year Published:2019
Deposited By:CODES ARC
Deposited On:2019-10-14
Last Modified:2019-12-10
Downloads:0

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