Resource and grade control model updating for underground mining production settings

Document Type

Journal Article

Publication Title

Mathematical Geosciences




Springer Link


School of Science




European Union's Horizon 2020 Research and Innovation Programme


Prior, Á., Benndorf, J., & Mueller, U. (2021). Resource and grade control model updating for underground mining production settings. Mathematical Geosciences, 53(4), 757-779. https://doi.org/10.1007/s11004-020-09881-2


© 2020, International Association for Mathematical Geosciences. 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.



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