Abstract
The Australian Joint Ore Reserves Committee (JORC) code guides public reporting to ensure effective communication of exploration and mining results. The JORC Code is also used in other jurisdictions. Recent insights of report compliance have highlighted issues (AIG, 2021; McManus et al., 2021; Sterk et al., 2019).
All three reviews focused on different aspects but commonly found that the “if not, why not” nature of the Code was mostly ignored in public reporting of mining projects. For example, one review found that no reports used quantitative assessments of the geological interpretation or mineral resource estimation envelopes, with some not addressing the quality of geological interpretation. Reports that did address interpretation quality used 19 different terms to communicate it. To reduce ambiguity in public reporting, it is essential to either reduce the possible terms that can be used to communicate quality or to quantify it.
Uncertainty assessment of spatial domains is required for compliance reporting to stock exchanges. There are a number of computational methods of assessing uncertainty that can be applied, but are rarely used, it is important that a body of literature highlight examples for industry acceptance. Recently, machine learning techniques (Bayesian approximation) have been used to assess the interpretation uncertainty of spatial domains. To improve its use in early-stage projects fast and cost effective pXRF data has also been compared favourably with ICP.
Recent work has also compared geostatistical simulation and machine learning to determine if the methods assess similar uncertainties. It was found that they highlight different uncertainties in geological models.
This poster highlights recent computational applications for use in industry to assess geological interpretation uncertainty, improve compliance with JORC and other public reporting codes, and facilitate communication of geological models' quality to both public investors and downstream users of mineral resource estimates.
All three reviews focused on different aspects but commonly found that the “if not, why not” nature of the Code was mostly ignored in public reporting of mining projects. For example, one review found that no reports used quantitative assessments of the geological interpretation or mineral resource estimation envelopes, with some not addressing the quality of geological interpretation. Reports that did address interpretation quality used 19 different terms to communicate it. To reduce ambiguity in public reporting, it is essential to either reduce the possible terms that can be used to communicate quality or to quantify it.
Uncertainty assessment of spatial domains is required for compliance reporting to stock exchanges. There are a number of computational methods of assessing uncertainty that can be applied, but are rarely used, it is important that a body of literature highlight examples for industry acceptance. Recently, machine learning techniques (Bayesian approximation) have been used to assess the interpretation uncertainty of spatial domains. To improve its use in early-stage projects fast and cost effective pXRF data has also been compared favourably with ICP.
Recent work has also compared geostatistical simulation and machine learning to determine if the methods assess similar uncertainties. It was found that they highlight different uncertainties in geological models.
This poster highlights recent computational applications for use in industry to assess geological interpretation uncertainty, improve compliance with JORC and other public reporting codes, and facilitate communication of geological models' quality to both public investors and downstream users of mineral resource estimates.
| Original language | English |
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| Publication status | Published - 30 Sept 2022 |