DATA ADAPTATION: A MINIMAL APPROACH FOR REDUCING GLOBAL SPATIAL UNCERTAINTY IN RESOURCE ESTIMATION

Spatial uncertainty in resource estimation can be managed by the adequacy of data, the reliability of domains, and the robustness of estimators. A common misconception is that more interpolations, less the uncertainty, however, interpolations primarily improve local data density. This study proposes quantified methods to improve boundary stability and interpolation robustness, and reduce spatial uncertainty by eliminating potential but unstable results. Additionally, this research suggests a minimal approach of reducing uncertainty by adapting data with stable geometric domains and geostatistical estimators. The application of data adaptation during estimating of morphologically complex and polymetallic deposits with extensive datasets shows significant improvements. Data adaptation can also efficiently utilize geological understandings and provide quantitative feedback throughout the process.