A computational/analytics framework for assessing the value of drill-hole information in ore grade estimation is described using Gaussian Process and statistics. A distinguishing feature is that it presents both a near-term and long-term vision, circumvents conditional simulations and avoids making rigid assumptions such as stationarity and uncorrelated errors. Two experiments are devised to cater for situations where geological domains are differentiated or mixed. In scenario 1, performance (learning) curves are obtained to inform in-fill drilling and spacing consideration consistent with current practice. Analysis shows it is possible to estimate the incremental cost and reward via a proxy measure without relying on the ground truth, using insights obtained from a similar deposit, adjacent bench or domain. Scenario 2 examines adaptive sampling strategies and focuses on applying these in geologically complex areas with discontinuities and heterogeneous composition. Evaluation is made based on structural similarity, the mean and uncertainty in the posterior predictive distribution for the grade. The results highlight situations where regular grid sampling is suboptimal, and demonstrate an adaptive strategy that targets spatial complexity is capable of narrowing this gap. The proposed methodology can potentially be used in the future in an exploration--exploitation setting that involves sampling, machine learning, reasoning and cooperation between robots with embodied intelligence on a mine site.

翻译：本文基于高斯过程与统计学方法，构建了一种用于评估钻孔信息在矿石品位估算中价值的计算/分析框架。其显著特征在于兼顾短期与长期视角，规避条件模拟，并避免做出如平稳性、误差不相关等刚性假设。为适应地质域存在差异或混合的情境，本文设计了两类实验。场景一通过获取性能（学习）曲线，指导符合现行实践的加密钻孔与间距设计。分析表明，利用相似矿床、相邻矿段或地质域的洞见，可通过代理指标估算增量成本与收益，而无需依赖地面真实值。场景二聚焦自适应采样策略，重点将其应用于具有不连续性及异质性成分的复杂地质区域。评估基于结构相似性指标、品位后验预测分布的均值与不确定性展开。结果揭示了规则网格采样在特定情境下的非最优性，并证明以空间复杂度为目标的适应性策略能够缩小这一差距。本文提出的方法未来可应用于涉及采样、机器学习、推理及具身智能机器人协同的矿山勘探-开采双阶段场景。