University of Leicester
Browse
Nathwani2022_Article_MachineLearningForGeochemicalE.pdf (4.99 MB)

Machine learning for geochemical exploration: classifying metallogenic fertility in arc magmas and insights into porphyry copper deposit formation

Download (4.99 MB)
journal contribution
posted on 2022-03-01, 11:57 authored by Chetan L. Nathwani, Jamie J. Wilkinson, George Fry, Robin N. Armstrong, Daniel J. Smith, Christian Ihlenfeld
A current mineral exploration focus is the development of tools to identify magmatic districts predisposed to host porphyry copper deposits. In this paper, we train and test four, common, supervised machine learning algorithms: logistic regression, support vector machines, artificial neural networks (ANN) and Random Forest to classify metallogenic ‘fertility’ in arc magmas based on whole-rock geochemistry. We outline pre-processing steps that can be used to mitigate against the undesirable characteristics of geochemical data (high multicollinearity, sparsity, missing values, class imbalance and compositional data effects) and therefore produce more meaningful results. We evaluate the classification accuracy of each supervised machine learning technique using a tenfold cross-validation technique and by testing the models on deposits unseen during the training process. This yields 81–83% accuracy for all classifiers, and receiver operating characteristic (ROC) curves have mean area under curve (AUC) scores of 87–89% indicating the probability of ranking a ‘fertile’ rock higher than an ‘unfertile’ rock. By contrast, bivariate classification schemes show much lower performance, demonstrating the value of classifying geochemical data in high dimension space. Principal component analysis suggests that porphyry-fertile magmas fractionate deep in the arc crust, and that calc-alkaline magmas associated with Cu-rich porphyries evolve deeper in the crust than more alkaline magmas linked with Au-rich porphyries. Feature analysis of the machine learning classifiers suggests that the most important parameters associated with fertile magmas are low Mn, high Al, high Sr, high K and listric REE patterns. These signatures further highlight the association of porphyry Cu deposits with hydrous arc magmas that undergo amphibole fractionation in the deep arc crust.

Funding

This work was supported by a Science and Solutions for a Changing Planet doctoral studentship, funded by the Natural Environment Research council (grant NE/L002515/1) and Anglo American. J. J. W., R. N. A. and D. J. S. acknowledge funding under Natural Environment Research Council grant (NE/P017452/1) ‘From arc magmas to ores (FAMOS): A mineral systems approach’.

History

Citation

Nathwani, C.L., Wilkinson, J.J., Fry, G. et al. Machine learning for geochemical exploration: classifying metallogenic fertility in arc magmas and insights into porphyry copper deposit formation. Miner Deposita (2022).

Author affiliation

Department of Geology

Version

  • VoR (Version of Record)

Published in

Mineralium Deposita

Publisher

Springer Science and Business Media LLC

issn

0026-4598

eissn

1432-1866

Acceptance date

2021-11-23

Copyright date

2022

Available date

2022-03-01

Language

en

Usage metrics

    University of Leicester Publications

    Categories

    No categories selected

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC