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Geosciences
Volume 10
Issue 2
10.3390/geosciences10020063
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Article

Geochemical Discrimination of Monazite Source Rock Based on Machine Learning Techniques and Multinomial Logistic Regression Analysis

by
Keita Itano
1,*,
Kenta Ueki
2,
Tsuyoshi Iizuka
3 and
Tatsu Kuwatani
2,4
1
School of Geosciences and Civil Engineering, Kanazawa University, Kanazawa 920-1192, Japan
2
Research Institute for Marine Geodynamics, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka 237-0061, Japan
3
Department of Earth and Planetary Science, The University of Tokyo, Tokyo 113-0033, Japan
4
PRESTO, Japan Science and Technology Agency (JST), Kawaguchi 332-0012, Japan
*
Author to whom correspondence should be addressed.
Geosciences 2020, 10(2), 63; https://doi.org/10.3390/geosciences10020063
Received: 4 December 2019 / Revised: 26 January 2020 / Accepted: 5 February 2020 / Published: 6 February 2020
(This article belongs to the Special Issue Chemical and Isotopic Signatures of Sediments: What do they tell us about the geological history of the Earth?)
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Abstract

Detrital monazite geochronology has been used in provenance studies. However, there are complexities in the interpretation of age spectra due to their wide occurrence in both igneous and metamorphic rocks. We use the multinomial logistic regression (MLR) and cross-validation (CV) techniques to establish a geochemical discrimination of monazite source rocks. The elemental abundance-based geochemical discrimination was tested by selecting 16 elements from granitic and metamorphic rocks. The MLR technique revealed that light rare earth elements (REEs), Eu, and some heavy REEs are important discriminators that reflect elemental fractionation during magmatism and/or metamorphism. The best model yielded a discrimination rate of ~97%, and the CV method validated this approach. We applied the discrimination model to detrital monazites from African rivers. The detrital monazites were mostly classified as granitic and of garnet-bearing metamorphic origins; however, their proportion of metamorphic origin was smaller than the proportion that was obtained by using the elemental-ratio-based discrimination proposed by Itano et al. in Chemical Geology (2018). Considering the occurrence of metamorphic rocks in the hinterlands and the different age spectra between monazite and zircon in the same rivers, a ratio-based discrimination would be more reliable. Nevertheless, our study demonstrates the advantages of machine-learning-based approaches for the quantitative discrimination of monazite. View Full-Text
Keywords: monazite; machine learning; rare earth element; trace element monazite; machine learning; rare earth element; trace element
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MDPI and ACS Style

Itano, K.; Ueki, K.; Iizuka, T.; Kuwatani, T. Geochemical Discrimination of Monazite Source Rock Based on Machine Learning Techniques and Multinomial Logistic Regression Analysis. Geosciences 2020, 10, 63. https://doi.org/10.3390/geosciences10020063

AMA Style

Itano K, Ueki K, Iizuka T, Kuwatani T. Geochemical Discrimination of Monazite Source Rock Based on Machine Learning Techniques and Multinomial Logistic Regression Analysis. Geosciences. 2020; 10(2):63. https://doi.org/10.3390/geosciences10020063

Chicago/Turabian Style

Itano, Keita, Kenta Ueki, Tsuyoshi Iizuka, and Tatsu Kuwatani. 2020. "Geochemical Discrimination of Monazite Source Rock Based on Machine Learning Techniques and Multinomial Logistic Regression Analysis" Geosciences 10, no. 2: 63. https://doi.org/10.3390/geosciences10020063

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