A workflow for assessing interpretation uncertainty in spatial domains using Bayesian approximation

In the mining industry, code compliant reporting standards for public announcements have been developed setting minimum standards for public reporting of Exploration Results and Mineral Resources. These include an assessment of the quality and confidence in the data and work carried out since public reporting aims to provide information that is Material, Transparent and Competent to investors.

There are four phases required to estimate a Mineral Resource (Preparation, Investigation, Model Creation and Validation), and estimation is highly dependent on the accuracy of the Preparation stage which is a result of the quality of the geological interpretation given for the mineralisation process and current spatial location. This interpretation seeks to spatially define geologically homogenous areas in the resource (spatial domains), corresponding to a single statistical population with a single orientation, where possible. In the estimation workflow, the creation of the spatial domain presents a challenge in terms of assessing the uncertainty in the geological interpretation often due to the manual and subjective interpretation, often incorporating expert opinion, used to guide its creation as well as in spatial domains with several mineralisation overprint events. Projects, where implicit modelling of spatial domains has been utilised, can also benefit from uncertainty assessment of predicted classification of drill hole intercepts.

A method has been developed using Bayesian Approximation to assess interpretation of drill-hole intercepts to spatial domains, using available quantitative and qualitative data. In the test study, portable XRF measurements were used parallel to visually logged data to test if pXRF multivariate data could also help assess interpretations where there is limited qualitative data, only grade element data or there are quality questions in regards to visual logging.