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.

采矿业的一项关键要求是表征矿床中矿石和废物的地质冶金特性的空间分布。由于地质的不确定性，资源模型只是现实的粗略表示，其预测价值有限。因此，在生产过程中收集的信息在采矿生产链中具有很高的价值。矿山规划模型通常基于矿物提取过程初始阶段的勘探信息。将具有不同支持的数据集成到资源或等级控制模型中可实现连续更新，并能够提供本地更准确的估算值。本文提出了一种更新算法，该算法集成了两种类型的传感器信息：裸露矿井特征的传感器，以及安装在传送带中的传感器。通过基于从德国弗赖贝格的银铅锌地下矿山Reiche-Zeche收集的信息的案例研究，分析了更新算法的影响。该算法是针对坡度控制模型的几种情况实现的。每个场景在提取之前代表不同级别的条件信息：没有条件信息，在开采面板外围的条件信息，以及在周边以及与面板交叉的钻孔的条件信息。进行分析以比较通过针对不同方案进行更新而获得的改进。显而易见的是，挖掘之前的条件信息级别不会影响两个或三个更新步骤之后的更新性能。更新算法的学习效果非常快，并覆盖了条件信息。