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Applied Bayesian modeling for assessment of interpretation uncertainty in spatial domains. (English) Zbl 1444.62068

Rahman, Azizur (ed.), Statistics for data science and policy analysis. Proceedings of the applied statistics and policy analysis conference 2019, ASPAC2019, Wagga Wagga, Australia, September 5–6, 2019. Singapore: Springer. 3-13 (2020).
Summary: 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 an 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 mineralization process and current spatial location. Performance of feasibility studies in mining projects has been poor, with a 50% failure rate, 17% of failures are attributable to issues in geological interpretation. 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 used to guide its creation as well as in spatial domains with several mineralization overprint events.
The proposed work investigates a Bayesian method using multivariate quantitative data combined with qualitative data to assess the interpretation uncertainty of classification of borehole intervals to a spatial domain defined by a 3D ‘wireframe’ or ‘rock type’ model interpretation using either implicit or explicit modeling techniques.
For the entire collection see [Zbl 1443.62006].

MSC:

62H11 Directional data; spatial statistics
62R07 Statistical aspects of big data and data science
62P30 Applications of statistics in engineering and industry; control charts

Software:

aplore3; brms; R
PDFBibTeX XMLCite
Full Text: DOI

References:

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