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Precursors predicted by artificial neural networks for mass balance calculations: quantifying hydrothermal alteration in volcanic rocks

Trépanier Sylvain, Mathieu Lucie, Daigneault Réal et Faure Stéphane. (2016). Precursors predicted by artificial neural networks for mass balance calculations: quantifying hydrothermal alteration in volcanic rocks. Computers & Geosciences, 89, p. 32-43.

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URL officielle: https://doi.org/10.1016/j.cageo.2016.01.003

Résumé

This study proposes an artificial neural networks-based method for predicting the unaltered (precursor) chemical compositions of hydrothermally altered volcanic rock. The method aims at predicting precursor’s major components contents (SiO2, FeOT, MgO, CaO, Na2O, and K2O). The prediction is based on ratios of elements generally immobile during alteration processes; i.e. Zr, TiO2, Al2O3, Y, Nb, Th, and Cr, which are provided as inputs to the neural networks. Multi-layer perceptron neural networks were trained on a large dataset of least-altered volcanic rock samples that document a wide range of volcanic rock types, tectonic settings and ages. The precursors thus predicted are then used to perform mass balance calculations. Various statistics were calculated to validate the predictions of precursors’ major components, which indicate that, overall, the predictions are precise and accurate. For example, rank-based correlation coefficients were calculated to compare predicted and analysed values from a least-altered test dataset that had not been used to train the networks. Coefficients over 0.87 were obtained for all components, except for Na2O (0.77), indicating that predictions for alkali might be less performant. Also, predictions are performant for most volcanic rock compositions, except for ultra-K rocks. The proposed method provides an easy and rapid solution to the often difficult task of determining appropriate volcanic precursor compositions to rocks modified by hydrothermal alteration. It is intended for large volcanic rock databases and is most useful, for example, to mineral exploration performed in complex or poorly known volcanic settings. The method is implemented as a simple C++ console program

Type de document:Article publié dans une revue avec comité d'évaluation
Volume:89
Pages:p. 32-43
Version évaluée par les pairs:Oui
Date:Avril 2016
Sujets:Sciences naturelles et génie > Sciences naturelles > Sciences de la terre (géologie, géographie)
Département, module, service et unité de recherche:Départements et modules > Département des sciences appliquées > Unité d'enseignement en sciences de la Terre
Mots-clés:hydrothermal alteration, immobile elements, mineral exploration, neural networks, multi-layer perceptron, mass balance calculations, altération hydrothermale, éléments immobiles, exploration minérale, réseaux de neurones, perceptron multicouche, calculs de bilan massique
Déposé le:12 sept. 2018 00:12
Dernière modification:12 sept. 2018 00:12
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