In the field of mineral resources extraction, one main challenge is to meet production targets in terms of geometallurgical properties. These properties influence the processing of the ore and are often represented in resource modeling by coregionalized variables with a complex relationship between them. Valuable data are available about geometalurgical properties and their interaction with the beneficiation process given sensor technologies during production monitoring. The aim of this research is to update resource models as new observations become available. A popular method for updating is the ensemble Kalman filter. This method relies on Gaussian assumptions and uses a set of realizations of the simulated models to derive sample covariances that can propagate the uncertainty between real observations and simulated ones. Hence, the relationship among variables has a compositional nature, such that updating these models while keeping the compositional constraints is a practical requirement in order to improve the accuracy of the updated models. This paper presents an updating framework for compositional data based on ensemble Kalman filter which allows us to work with compositions that are transformed into a multivariate Gaussian space by log-ratio transformation and flow anamorphosis. This flow anamorphosis, transforms the distribution of the variables to joint normality while reasonably keeping the dependencies between components. Furthermore, the positiveness of those variables, after updating the simulated models, is satisfied. The method is implemented in a bauxite deposit, demonstrating the performance of the proposed approach.

A key requirement for the mining industry is the characterization of the spatial distribution of geometallurgical properties of the ore and waste in a mineral deposit. Due to geological uncertainty, resource models are crude representations of reality, and their value for forecasting is limited. Information collected during the production process is therefore of high value in the mining production chain. Models for mine planning are usually based on exploration information from an initial phase of the mineral extraction process. The integration of data with different supports into the resource or grade control model allows for continuous updating and is able to provide estimates that are more accurate locally. In this paper, an updating algorithm is presented that integrates two types of sensor information: sensors characterizing the exposed mine face, and sensors installed in the conveyor belt. The impact of the updating algorithm is analysed through a case study based on information collected from Reiche-Zeche, a silver–lead–zinc underground mine in Freiberg, Germany. The algorithm is implemented for several scenarios of a grade control model. Each scenario represents a different level of conditioning information prior to extraction: no conditioning information, conditioning information at the periphery of the mining panel, and conditioning information at the periphery and from boreholes intersecting the mining panel. Analysis is performed to compare the improvement obtained by updating for the different scenarios. It becomes obvious that the level of conditioning information before mining does not influence the updating performance after two or three updating steps. The learning effect of the updating algorithm kicks in very quickly and overwrites the conditioning information.