Application of Resource and Grade Control Model updating by Univariate and Compositional Sequential Ensemble Filtering


Application of Resource and Grade Control Model updating by Univariate and Compositional Sequential Ensemble Filtering

Prior-Arce, A.; Bendorf, J.; Tolosana-Delgado, R.; Verlaan, M.

A key requirement for mining operations is the need to be planned according to the existence knowledge about the geometallurgical features of the mineral resource. Geostatistical models are created out of exploration data to describe such geological features as a resource and grade control model. To collect information during the mining production process about these features is becoming more popular due to sensor technology advances. In order to incorporate this new information during the mining production process to the geostatistical models, new approaches based on data assimilation theory are being developed. These are Ensemble Sequential Updating techniques. They provide a comprehensive solution for these challenges since, that allow to relate the potentially non-linear relation that exists between the resource and grade control model state variables and the observations.

However, there are different challenges to face in order to understand how the information that proceed from sensors informs about the geostatistical models and how to feed updated information back to the resource model. On one hand, the state variables of such models are actually compositions, mineral and/or chemical. Classical data assimilation solutions need thus to be modified to account for mass conservation of each component, while keeping their physical relations, univariate or multi-variable (e.g., stability of mineral assemblages, total sum constraint, positivity). On the other hand, some sensors might deliver information restricted to a subcomposition. A compositional data assimilation approach supersede some of these problems by dealing with the positivity condition and the mass preservation implicitly through assimilating log-ratios instead of the original components, which naturally allow a subcompositionally coherent modification if the sensors used require it.

The univariate and the compositional data assimilation approaches are tested in two different virtual assets models created as a fully controllable environment with different geometallurgical features. After validation, a sensitivity analysis investigates the effects of different parameters and derived practical implementation aspects for an effective application of compositional data assimilation within an operating mine.

Keywords: Geostatistics; Data Assimilation

  • Contribution to proceedings
    IAMG 2017 18th Annual Conference, 02.-09.09.2017, Perth, Australia
    Proceedings of IAMG 2017

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Publ.-Id: 28848