Real-Time Updating of Geochemical and Geometallurgical Spatial Models with Multivariate Ensemble Kalman Filtering: Application to Golgohar Iron Deposit

This paper presents an updatable stochastic geometallurgical framework that integrates geochemical compositions and processing-related variables within a unified spatial modeling and data assimilation workflow. The framework combines multivariate geostatistical simulation with real-time updating based on the Ensemble Kalman Filter, allowing stochastic realizations to be sequentially adjusted as new production data become available. The methodology accounts for geological uncertainty, compositional constraints, and multivariate dependencies. This is achieved by combining the isometric log-ratio transformation with flow anamorphosis within a multivariate Gaussian framework. As a result, compositional geochemical variables and metallurgical responses can be updated consistently while preserving their physical and statistical relationships. The framework is demonstrated using the Gol Gohar iron ore deposit as a case study. Exploration drill hole data and production-scale blast hole measurements are assimilated within an ore control context. The results indicate that the update-enabled simulation approach reduces prediction errors and spatial uncertainty, while capturing complex, non-linear relationships among geometallurgical variables. The framework is generic and can be applied to other deposits where real-time integration of geological, geochemical, and processing information is needed to support operational decision-making.