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Machine Learning Prediction of Ore Deposit Genetic Type Using Magnetite Geochemistry

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Abstract

Magnetite geochemistry is crucial for the discrimination of ore deposit genetic type. Traditional two-dimensional discrimination diagrams based on particular data for limited deposits cannot meet the requirements of high-precision classification. The continuous compilation of magnetite geochemical big data and high-precision machine learning algorithms provide a new research route. In this study, a laser ablation inductively coupled plasma mass spectrometry (LA–ICP–MS) magnetite geochemical dataset compiled from published literature for different geological environments worldwide was used to train different machine learning (ML) classifiers, including random forest, support vector machine, and multilayer perceptron neural network, to predict the genetic type of ore deposits. To verify the efficacy of the classifier, LA–ICP–MS analysis was performed on magnetite samples collected from the Makeng and Luoyang iron deposits in Fujian Province, southeastern China. The obtained data were used to predict the ore deposit genetic type with the aid of the ML classifiers. The classifiers established by all three algorithms yielded good predictive outcomes. The results were consistent with those obtained from detailed petrogeochemical and isotopic studies, showing that the deposits are skarn-type, validating the suitability of the classifier. This article also provides an executable program of the classifiers for anyone to use. The executable program meets the requirements for ore deposit-type classification and can be considered a powerful tool for magnetite exploration and prospecting.

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Data Availability

The online version contains supplementary materials (archived Table S1 (LA–ICP–MS trace element compositions of magnetite from published literature), S2 (Processed LA–ICP–MS data used to build the model), S3 (The training data in the study), S4 (Electron probe microanalysis (wt.%) of magnetite from the Makeng and Luoyang Fe deposits), S5 (LA–ICP–MS trace element compositions of magnetite from the Makeng and Luoyang Fe deposits), S6 (All hyperparameters in the models and the optimal values), S7 (Classification report for the RF and SVM classifiers (without removing outliers)), S8 (Test set data for Chuquicamata porphyry deposit), S9 (Test set data for Kalatonke magmatic Cu–Ni sulfide deposit), S10 (Test set data for the Makeng deposit), S11 (Test set data for the Luoyang deposit), and Figure S1 (Electron microscope images of magnetite from the Makeng and Luoyang deposits), S2 (Learning curve of the RF, SVM and MLP algorithm), S3(Magnetite trace element concentrations for different deposit types (without removing outliers)), S4 (Confusion matrices of the validation set (without removing outliers)) and an executable program (Magnetite_Classifier)) available at https://github.com/Peng2001200173/Supplementary_Material.git.

Code Availability

Names of the codes: MLP classifier, RF classifier, and SVM classifier. License type: GNU General Public License v3.0. Running the codes provided in this study requires a Python environment and pandas and scikit-learn libraries. The source codes are available for downloading at the link https://github.com/Peng2001200173/Classifier.git.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China [grant numbers 42050103 and 41702075], the National Key R&D Program of China [grant number 2018YFE0204204], and the Fundamental Research Funds for the Central Universities [grant number 2652018132].

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  1. School of Earth Sciences and Resources, China University of Geosciences, Beijing, 100083, China

    Peng Zhang, Zhenjie Zhang & Qiuming Cheng

  2. State Key Lab of Geological Processes and Mineral Resources, China University of Geosciences, Beijing, 100083, China

    Zhenjie Zhang, Jie Yang & Qiuming Cheng

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Zhang, P., Zhang, Z., Yang, J. et al. Machine Learning Prediction of Ore Deposit Genetic Type Using Magnetite Geochemistry. Nat Resour Res 32, 99–116 (2023). https://doi.org/10.1007/s11053-022-10146-4

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  • DOI: https://doi.org/10.1007/s11053-022-10146-4

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