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\begin{document}

\title{A practical approach for tectonic discrimination of basalts using
geochemical data through machine learning}



\author[1]{Mengqi Gao}%
\author[2]{Zhaochong Zhang}%
\author[1]{Xiaohui Ji}%
\author[1]{Hengxu Li}%
\author[2]{Zhiguo Cheng}%
\author[1]{M. Santosh}%
\affil[1]{China University of Geosciences, Beijing}%
\affil[2]{State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Beijing}%


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  \date{June 03, 2024}


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\begin{abstract}
Identifying the tectonic setting of formation of rocks is an essential
component in the field of geosciences. The conventional approach is to
employ standard tectonic discrimination diagrams based on elemental
correlations and ratios, which sometimes are plagued with uncertainties
and limitations. The application of machine learning algorithms based on
big data can effectively overcome these problems. In this study, three
machine learning algorithms, namely Support Vector Machine, Random
Forest, and XGBoost, were employed to classify the various types of
basalts from diverse settings such as intraplate basalts, island arc
basalts, ocean island basalts, mid-ocean ridge basalts, back-arc basin
basalts, oceanic flood basalts, and continental flood basalts into seven
tectonic environments. For the altered basalts and fresh basalt, we use
22 relatively immobile elements (TiO2, P2O5, Nb, Ta, Zr, Hf, Y, La, Ce,
Pr, Nd, Sm, Eu, Gd, Ho, Er, Yb, Lu, Dy, Tb, Cr, Ni) and 35 major plus
trace elements to build discrimination models for seven types of
tectonic settings of basalt, respectively. The results indicate that
XGBoost demonstrates the best performance in discriminating basalts into
seven tectonic settings, achieving an accuracy of 85\% and 89\%
respectively. Compared to previous models, our new method presented in
this study is expected to have better practical applications.%
\end{abstract}%



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\textbf{Hosted file}

\verb`A practical approach for tectonic discrimination of basalts using geochemical data through machine learning.docx`  available at \url{https://authorea.com/users/749825/articles/721003-a-practical-approach-for-tectonic-discrimination-of-basalts-using-geochemical-data-through-machine-learning}

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