Over the past decade, the rapid updating of resource knowledge and the integration of real-time sensor information have gained attention in both industry and academia. However, most studies on rapid resource model updating have focused on continuous variables, such as grade variables and coal quality parameters. Geological domain modelling is an essential component of resource estimation, which is why it is crucial to extend data assimilation techniques to enable the rapid updating of categorical variables. In this paper, a methodology inspired by pluri-Gaussian simulation is proposed for near-real-time updating of geological domains, followed by updating grade variables within these domain boundaries. The proposed algorithm consists of a Gibbs sampler for converting geological domains into Gaussian random fields, an ensemble Kalman filter with multiple data assimilations for rapid updating, and rotation based iterative Gaussianisation for multi-Gaussian transformation. We demonstrate the algorithm by using a synthetic case study with observations sampled from the ground truth, as well as a real case study that uses production drilling samples to jointly update geological domains and grade variables. Both case studies are based on real data from an iron oxide-copper-gold deposit in South Australia. This approach enhances resource knowledge by incorporating both categorical and continuous variables, leading to improved reproduction of domain geometries, closer matches between predictions and observations, and more geologically realistic resource models.

翻译：过去十年间，资源认知的快速更新与实时传感器信息的集成在工业界和学术界均受到广泛关注。然而，现有关于资源模型快速更新的研究大多集中于连续变量，如品位变量和煤质参数。地质域建模是资源估算的核心组成部分，因此将数据同化技术拓展至分类变量的快速更新至关重要。本文提出一种受多元高斯模拟启发的近实时地质域更新方法，随后在更新后的地质域边界内对品位变量进行同步更新。该算法包含三个核心部分：通过吉布斯采样器将地质域转化为高斯随机场，采用多重数据同化的集成卡尔曼滤波器实现快速更新，以及基于旋转的迭代高斯化方法完成多元高斯变换。我们通过两个案例验证算法有效性：一是基于真实数据采样的合成案例研究，二是利用生产钻孔样本联合更新地质域与品位变量的实际案例研究。两项案例均基于南澳大利亚某铁氧化物-铜-金矿床的真实数据。该方法通过融合分类变量与连续变量，增强了资源认知能力，实现了更精确的地质域几何形态重建、预测与观测值的高度匹配，以及更具地质合理性的资源模型。