Assessment of magmatic fertility using pXRF on altered rocks from the Ordovician Macquarie Arc, New South Wales

Wells, Tristan; Meffre, Sébastien; Cooke, David R.; Steadman, Jeffrey A.; Hoye, J. L.

doi:10.1080/08120099.2020.1782471

Cited by 7 publications

(4 citation statements)

References 43 publications

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“…In typical in situ pXRF work on volcanic and intrusive rock samples, it has become common practice to average several measurement spots for each sample, to account for mineralogical heterogeneity (e.g., Bourke and Ross, 2016;McNulty et al, 2018McNulty et al, , 2020Ahmed et al, 2020;Jimenez-Gonzalez, 2020;Adams et al, 2021;Wells et al, 2021). To facilitate shifting the sample by a fixed distance between each analytical spot, a grid with a spacing of 12 mm was placed at the bottom of the workstation (Fig.…”

Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%

Portable XRF applied to regional bedrock mapping in Quebec, Canada

Ross,

Beaudette,

Daoudene

2024

Journal of Geochemical Exploration

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Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%

Portable XRF applied to regional bedrock mapping in Quebec, Canada

Ross,

Beaudette,

Daoudene

2024

Journal of Geochemical Exploration

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“…Additional elements (e.g. V, Sc, Nb and Zr; Loucks 2014; Wells et al 2021) have previously been found to act as useful discriminants; however, here we restrict our element list to those that have the most complete records in (2010a, 2010b) the database, and are least subject to incomplete analyses and missing values. The use of composite datasets (e.g.…”

Section: Data Compilation and Quality Controlmentioning

confidence: 99%

“…Generally, a partly hydrothermally influenced classification model is still empirically useful, but clouds the underlying process understanding and may diminish classification accuracy because of the potential for significant spatial variability in alteration effects (Ulrich and Heinrich 2002;Cooke et al 2014). Furthermore, these effects can partly to strongly obscure certain igneous signatures; for example, the mobility of Sr can impede its use in fingerprinting magma fertility (Wells et al, 2021). These hydrothermal effects are more likely for the most fluid mobile components, such as K, Sr, Mn, Ca and Fe.…”

Section: Machine Learning Biases and Additional Influencesmentioning

confidence: 99%

“…This leads to rocks having the characteristics noted above, which can then be discriminated from normal arc rocks using bivariate thresholds (e.g. Loucks 2014;Ahmed et al 2020;Wells et al 2021). Such classification methods are useful but limited because they ignore additional variables that may hold fertility signals and lead to false positive outcomes because other processes can generate similar geochemical signatures.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Gardaneh Salavat porphyry copper system: Is it about metal endowment or fertility of the porphyritic intrusion?

Mashhadi

2022

Acta Geochim

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Assessment of magmatic fertility using pXRF on altered rocks from the Ordovician Macquarie Arc, New South Wales

Cited by 7 publications

References 43 publications

Portable XRF applied to regional bedrock mapping in Quebec, Canada

Portable XRF applied to regional bedrock mapping in Quebec, Canada

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

Gardaneh Salavat porphyry copper system: Is it about metal endowment or fertility of the porphyritic intrusion?

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