Applied Bayesian Modeling for Assessment of Interpretation Uncertainty in Spatial Domains

Scott McManus (), Azizur Rahman, Ana Horta and Jacqueline Coombes
Additional contact information
Scott McManus: Charles Sturt University
Azizur Rahman: Charles Sturt University, School of Computing and Mathematics
Ana Horta: Charles Sturt University
Jacqueline Coombes: Snowden Technologies

Chapter Chapter 1 in Statistics for Data Science and Policy Analysis, 2020, pp 3-13 from Springer

Abstract: Abstract 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.

Keywords: Bayesian; Uncertainty; Spatial domain (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-15-1735-8_1

Ordering information: This item can be ordered from
http://www.springer.com/9789811517358

DOI: 10.1007/978-981-15-1735-8_1

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().