Effective information gathering for ore estimation, evaluation and perspectives on adaptive sampling
This work addresses the problem of efficient ore grade estimation for mining operations, offering a method that avoids rigid assumptions and conditional simulations, though it remains domain-specific and incremental.
The paper presents a Gaussian Process-based framework for evaluating drill-hole information in ore grade estimation, demonstrating that adaptive sampling strategies targeting spatial complexity outperform regular grid sampling in geologically complex areas. Results show the framework can estimate incremental cost and reward without ground truth, using proxy measures from similar deposits.
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.