Resource and Grade Control Model Updating for Underground Mining Production Settings

A key requirement for the mining industry is to characterize 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 of limited value in forecasting. Information collected during the production process is therefore highly valued 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 sensor data measured at different support along the production line into the resource or grade control model allows for continuous updating and has the ability to provide estimates that are locally more accurate.

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 for a case study based on information collected from Reiche-Zeche a silver-lead-zinc underground mine in Freiberg, Germany.

The algorithm has been implemented for several scenarios of a grade control models. Each scenario represents a different level of conditioning information prior extraction: no conditioning information, conditioning information at the periphery of the mining panel, and lastly at the periphery and from bore-holes intersecting the mining panel. An analysis compares the improvement obtained by updating for the different scenarios. It become 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 fast and overwrites the conditioning information.