Stochastic Modelling of Mineral Exploration Targets  


Authors / CoAuthors

Talebi, H. | Mueller, U. | Peeters, L.J.M.. | Otto, A. | de Caritat, P. | Tolosana-Delgado, R. | van den Boogaart, K.G.

Abstract

Rapid, efficient, and accurate prediction of mineral occurrence that takes uncertainty into 20 account is essential to optimise defining exploration targets. Traditional approaches to mineral 21 potential mapping often fail to fully appreciate spatial uncertainties of input predictors and their 22 spatial cross-correlation. In this study a stochastic technique based on multivariate 23 geostatistical simulations and ensemble tree-based learners is introduced for predicting and 24 uncertainty quantification of mineral exploration targets. The technique is tested on a synthetic 25 case inspired by the characteristics of a hydrothermal mineral system model and a real-world 26 dataset from the Yilgarn Craton in Western Australia. Results from the two cases proved the 27 superior performance and robustness of the proposed stochastic technique, especially when 28 dealing with high dimensional and large data sets. <b>Citation:</b> Talebi, H., Mueller, U., Peeters, L.J.M. et al. Stochastic Modelling of Mineral Exploration Targets. <i>Math Geosci </i>54, 593–621 (2022). https://doi.org/10.1007/s11004-021-09989-z

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document

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131497

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Keywords

theme.ANZRC Fields of Research.rdf
  • Stochastic Analysis and ModellingEARTH SCIENCESExploration Geochemistry
  • machine Learning
  • predictive model
  • mineral potential mapping
  • spatial data
  • multivariate geostatistical simulation
  • Published_External

Publication Date

2023-10-26T22:45:18

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Australian Government Security ClassificationSystem    

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Creative Commons Attribution 4.0 International Licence    

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completed

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Manuscript for external publication

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asNeeded

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geoscientificInformation

Series Information

Mathematical Geosciences Volume 54, 2022 593–621

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Article submitted to Mathematical Geosciences journal

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Extents

[-44.00, -9.00, 112.00, 154.00]

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Spatial Resolution

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