Abstract

Spatial domains defining either geological models or mineral estimation envelopes are among the few components of the mining life cycle that are not quantitatively assessed to communicate uncertainty or error in mineral resource projects. Recent work has investigated the use of Bayesian approximation methods to assess interpretation uncertainty of the classification of drill hole intercepts to spatial domain categories. A fundamental assumption is that the spatial domains being tested are used for geostatistics and that the spatial domains contain data that is homogenous to satisfy an assumption of stationarity. A binomial or multinomial model of data used to generate the spatial domains can be trained and simulated to assess the model's ability to predict categories. Categorical subjective geological logging and multielement data have shown suitable data inputs to assess interpretation uncertainty at two case studies. In early-stage projects laboratory multielement data may not be available or economically feasible. Portable X-Ray Fluorescence (pXRF) is a cost and time-effective method of providing multielement data at lower precision and accuracy. A case study had both Inductively coupled plasma mass spectrometry (ICP) and pXRF data. Bayesian approximation models were created from both data sets to assess interpretation uncertainty. Visual assessment of uncertainty band graphs and statistical testing using the Bayesian correlated t-test shows that the two data sets' interpretation uncertainty models are practically equivalent and that pXRF data can be used in early stage projects to assess interpretation uncertainty.
Original languageEnglish
Journal Applied Computing and Geosciences
VolumeSeptember 2021
DOIs
Publication statusPublished - 02 Sep 2021

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