Rapid resource model updating with real-time data is important for making timely decisions in resource management and mining operations. This requires optimal merging of models and observations, which can be achieved through data assimilation, and the ensemble Kalman filter (EnKF) has become a popular method for this task. However, the modelled resources in mining usually consist of multiple variables of interest with multivariate relationships of varying complexity. EnKF is not a multivariate approach, and even for univariate cases, there may be slight deviations between its outcomes and observations. This study presents a methodology for rapidly updating multivariate resource models using the EnKF with multiple data assimilations (EnKF-MDA) combined with rotation based iterative Gaussianisation (RBIG). EnKF-MDA improves the updating by assimilating the same data multiple times with an inflated measurement error, while RBIG quickly transforms the data into multi-Gaussian factors. The application of the proposed algorithm is validated by a real case study with nine cross-correlated variables. The combination of EnKF-MDA and RBIG successfully improves the accuracy of resource model updates, minimises uncertainty, and preserves the multivariate relationships.

翻译：基于实时数据快速更新资源模型对于资源管理与采矿作业中及时决策至关重要。这需要实现模型与观测数据的最优融合，可通过数据同化达成，而集合卡尔曼滤波（EnKF）已成为该任务中广泛采用的方法。然而，采矿领域的建模资源通常包含多个关注变量，且其多元关系具有不同的复杂度。EnKF并非多元方法，即使在单变量情形下，其结果与观测值之间亦可能存在轻微偏差。本研究提出一种结合多重数据同化的EnKF（EnKF-MDA）与基于旋转的迭代高斯化（RBIG）的多元资源模型快速更新方法。EnKF-MDA通过以膨胀的测量误差多次同化相同数据来改进更新过程，而RBIG则能快速将数据转换为多高斯因子。通过一个包含九个互相关变量的实际案例研究，验证了所提算法的适用性。EnKF-MDA与RBIG的结合成功提升了资源模型更新的准确性，降低了不确定性，并保持了多元关系。