Raymond Leung

This article examines the use of data analytics in mining automation. It considers the benefits of integrating telemetry data in computational systems that model the state of a mine. Specifically, it focuses on interactions between load-haul vehicles and the information that may be harvested to facilitate material tracking in surface mining. Bucket dig positions are inferred from GPS data by analysing wheel-loader and excavator interactions with haul trucks. Evaluation shows this approach achieves higher precision and load-haul cycle recall, as extra buckets missing from the existing OEM source are discovered. Analysis reveals a consistent pattern of behaviour from equipment operators which justifies the use of a simple model that neglects the articulated motion of the excavator arm. A major contribution is the integration of two technical objectives: the ability to consistently locate excavation points within mining pits based on vehicle interactions, and describe compositional variation in the excavated material using accurate local grade models informed by geochemical assays. This allows material movement associated with individual load-dump events to be tracked and linked with the underlying geology via kriging estimates. The case study highlights the importance of having high-resolution data, as it enables transient ore dilution events (e.g. inadvertent transferral of waste to stockpiles) to be identified and factored into risk assessment. The information generated may be used for ore-properties tracking in a dynamic graphical model to capture correlations, uncertainties and compositional changes induced by material movement. Recent development and application opportunities including the role of machine learning are briefly discussed.

Probabilistic models are used to describe random processes and quantify prediction uncertainties in a principled way. Examples include geotechnical and geological investigations that seek to model subsurface hydrostratigraphic properties or mineral deposits. In mining geology, model validation efforts have generally lagged behind the development and deployment of computational models. One problem is the lack of industry guidelines for evaluating the uncertainty and predictive performance of probabilistic ore grade models. This paper aims to bridge this gap by developing a holistic approach that is autonomous, scalable and transferable across domains. The proposed model assessment targets three objectives. First, we aim to ensure that the predictions are reasonably calibrated with probabilities. Second, statistics are viewed as images to help facilitate large-scale simultaneous comparisons for multiple models across space and time, spanning multiple regions and inference periods. Third, variogram ratios are used to objectively measure the spatial fidelity of models. In this study, we examine models created by ordinary kriging and the Gaussian process in conjunction with sequential or random field simulations. The assessments are underpinned by statistics that evaluate the model’s predictive distributions relative to the ground truth. These statistics are standardised, interpretable and amenable to significance testing. The proposed methods are demonstrated using extensive data from a real copper mine in a grade estimation task and are accompanied by an open-source implementation. The experiments are designed to emphasise data diversity and convey insights, such as the increased difficulty of future-bench prediction (extrapolation) relative to in situ regression (interpolation). This work enables competing models to be evaluated consistently and the robustness and validity of probabilistic predictions to be tested, and it makes cross-study comparison possible irrespective of site conditions.

Probabilistic models are used extensively in geoscience to describe random processes as they allow prediction uncertainties to be quantified in a principled way. These probabilistic predictions are valued in a variety of contexts ranging from geological and geotechnical investigations to understanding subsurface hydrostratigraphic properties and mineral distribution. However, there are no established protocols for evaluating the uncertainty and predictive performance of univariate probabilistic models, and few examples that researchers and practitioners can lean on. This paper aims to bridge this gap by developing a systematic approach that targets three objectives. First, geostatistics are used to check if the probabilistic predictions are reasonable given validation measurements. Second, image-based views of the statistics help facilitate large-scale simultaneous comparisons for a multitude of models across space and time, for instance, spanning multiple regions and inference periods. Third, variogram ratios are used to objectively measure the spatial fidelity of models. In this study, the model candidates include ordinary kriging and Gaussian Process, with and without sequential or correlated random field simulation. A key outcome are recommendations that encompass the FLAGSHIP statistics which examine the fidelity, likelihood, accuracy, goodness, synchronicity, histogram, interval tightness and precision of the model predictive distributions. These statistics are standardised, interpretable and amenable to significance testing. The proposed methods are demonstrated using extensive data from a real copper mine in a grade estimation task, and accompanied by an open-source implementation. The experiments are designed to emphasise data diversity and convey insights, such as the increased difficulty of future-bench prediction (extrapolation) relative to in-situ regression (interpolation). This work presents a holistic approach that enables modellers to evaluate the merits of competing models and employ models with greater confidence by assessing the robustness and validity of probabilistic predictions under challenging conditions.

Probabilistic models are used extensively in geoscience to describe random processes as they allow prediction uncertainties to be quantified in a principled way. These probabilistic predictions are valued in a variety of contexts ranging from geological and geotechnical investigations to understanding subsurface hydrostratigraphic properties and mineral distribution. However, there are no established protocols for evaluating the uncertainty and predictive performance of univariate probabilistic models, and few examples that researchers and practitioners can lean on. This paper aims to bridge this gap by developing a systematic approach that targets three objectives. First, geostatistics are used to check if the probabilistic predictions are reasonable given validation measurements. Second, image-based views of the statistics help facilitate large-scale simultaneous comparisons for a multitude of models across space and time, for instance, spanning multiple regions and inference periods. Third, variogram ratios are used to objectively measure the spatial fidelity of models. In this study, the model candidates include ordinary kriging and Gaussian Process, with and without sequential or correlated random field simulation. A key outcome are recommendations that encompass the FLAGSHIP statistics which examine the fidelity, likelihood, accuracy, goodness, synchronicity, histogram, interval tightness and precision of the model predictive distributions. These statistics are standardised, interpretable and amenable to significance testing. The proposed methods are demonstrated using extensive data from a real copper mine in a grade estimation task, and accompanied by an open-source implementation. The experiments are designed to emphasise data diversity and convey insights, such as the increased difficulty of future-bench prediction (extrapolation) relative to in-situ regression (interpolation). This work presents a holistic approach that enables modellers to evaluate the merits of competing models and employ models with greater confidence by assessing the robustness and validity of probabilistic predictions under challenging conditions.

This survey article provides a synopsis of some of the engineering problems, technological innovations, robotic development, and automation efforts encountered in the mining industry, particularly in the Pilbara iron ore region of Western Australia. The goal is to paint the technology landscape and highlight issues relevant to an engineering audience to raise awareness of artificial intelligence (AI) and automation trends in mining. It assumes the reader has no prior knowledge of mining and builds context gradually through focused discussion and short summaries of common open-pit mining operations. The principal activities that take place may be categorized in terms of resource development and mine, rail, and port operations (Figure 1). From mineral exploration to ore shipment, there are roughly nine steps in between (Figure 2). These include geological assessment, mine planning and development, production drilling and assaying, blasting and excavation, transportation of ore and waste, crushing and screening, stockpiling and loading out, rail network distribution, and ore car dumping. The objective is to describe these processes and provide insights on some of the challenges/opportunities from the perspective of a decade-long industry–university R&D partnership.

For banded iron formation-hosted deposits accurate boundary modelling is critical to ore-grade estimation. Key to estimation fidelity is the accurate separation of the different domains within the ore body, requiring modelling of the boundaries between domains. This yields both theoretical and application challenges. We present a series of solutions for application challenges that arise when modelling large-scale boundaries employing a composition of Gaussian Process models on exploration and production hole data. We demonstrate these in the banded iron formation-hosted iron ore deposits in the Hamersley Province of Western Australia. We present solutions to several challenges: the inclusion of information derived from a geologist-defined boundary estimate to incorporate domain knowledge in data sparse regions, the incorporation of unassayed production holes that are implicitly defined as waste to augment production hole assay data, and a more holistic method of defining regional bounds and spatial rotations for Gaussian Process modelling of local spaces. Solution are evaluated against a range of metrics to show performance improvements over the manually performed estimation by an expert geologist of the boundaries delineating the ore body domains. Reconcilliation scores are used for evaluating the quality of predicted domain boundaries against measured production data. The predicted and in situ surfaces are also qualitatively evaluated against production data to ensure that the models were evaluated to be geologically sound by an expert in the field. In particular, better fidelity is shown when separating mineralised and non-mineralised ore, consequently improving the estimation of the ore-grades present in the mine site.