Multivariate Ensemble Based Sequential Update Of The Resource Model For Real Time Mine Settings

Mining industry is continuously monitoring key performance indicators (KPI), and geo-metallurgical properties such as grade, fragmentation or tonnage and reconciling estimates to online capture production performance parameters. New technology is looking for monitoring also other properties as grain size.
Relevant information is obtained from sensors installed at different mining production process stages, as in a Block Evaluation or Schedule and Blending.
This study aims to develop an efficient updating framework based on Sequential Ensemble Filtering by using compositional data statistics that will be able to cope with the non-linearities of the system.