Measures of grade heterogeneity, or the spatial distribution of grades, depend on the scale of sampling. At the resource modelling scale, heterogeneity measures are limited to the scale of the data used to estimate the model. As denser sampling becomes available (e.g., from blast holes immediately prior to mining), it is, in principle, possible to provide measures of heterogeneity at smaller scales to allow selective mining of large resource blocks. However, this can only be done if the local resource model can be updated rapidly with the newly acquired data in time for selectivity decisions to be made (e.g., selective blasting and loading from a resource block). The economic value of quantifying small-scale grade heterogeneity is significant in terms of mining selectivity and recoverability. This study proposes an approach, based on the Kalman filter, for near real-time resource model downscaling and updating by integrating additional data from production blast holes. In this approach, the model assimilates newly acquired data and generates measures of small-scale grade heterogeneity to provide a basis on which better selective mining and loading decisions can be made. A synthetic dataset is used to demonstrate and validate the algorithm. The results show that the proposed algorithm is capable of updating a resource model in near real time and identifying 68% of the small-scale grade variability within a mining block.

等级异质性的度量或等级的空间分布取决于抽样的规模。在资源建模规模上，异质性度量限于用于估计模型的数据规模。随着可获得更密集的采样（例如，从紧接在采矿之前的爆破孔中），原则上可以在较小规模上提供异质性度量，以允许对大型资源块进行选择性开采。但是，只有在可以及时用新获取的数据快速更新本地资源模型以进行选择性决策（例如，从资源块中进行选择性爆破和加载）时，才能执行此操作。量化小规模品位异质性的经济价值在采矿选择性和可采性方面具有重要意义。这项研究提出了一种方法，基于Kalman滤波器，可通过集成生产爆破孔中的其他数据进行近实时资源模型缩减和更新。在这种方法中，该模型吸收了新获取的数据，并生成了小规模品位异质性的度量，从而为可以做出更好的选择性采矿和装载决策提供了基础。综合数据集用于演示和验证算法。结果表明，该算法能够实时更新资源模型，并能识别出一个采矿区块内小规模品位变化的68％。该模型吸收了新获取的数据，并生成了小规模异质性的度量，从而为做出更好的选择性开采和装载决策提供了基础。综合数据集用于演示和验证算法。结果表明，该算法能够实时更新资源模型，并能识别出一个采矿区块内小规模品位变化的68％。该模型吸收了新获得的数据，并生成了小规模品位异质性的度量，从而为做出更好的选择性开采和装载决策提供了基础。综合数据集用于演示和验证算法。结果表明，该算法能够近实时地更新资源模型，并能识别出采矿区块内小规模品位变化的68％。