Challenges with Ore Body Knowledge: A Geologist’s Perspective
A conversation with Paul Hodkiewicz
Introduction
In the mining industry, ore body knowledge is the cornerstone of decision-making across the entire mining lifecycle—from exploration to mine closure. Understanding the nature and variability of ore bodies is essential to maximising resource extraction efficiency, minimising waste, and managing costs. However, the complexities inherent in ore deposits and technological and data integration challenges can make it difficult for geologists to develop accurate and reliable models. This discussion delves into the core challenges of increasing ore body knowledge and explores the technological, methodological, and collaborative approaches required to address them.
- Data Integration and Utilisation Challenges
A significant challenge in ore body knowledge arises from the disconnect between data collection technologies and their effective utilisation. The mining industry now has access to advanced sensors and high-resolution scanning technologies, generating vast amounts of geological data. However, this data is not yet well utilised due to a lack of translation between data providers and end-users like resource geologists and modellers.
For instance, while many companies deploy extensive scanning and sensing technologies, they often need help to transform this raw data into actionable insights. The absence of specialised services or tools for effectively integrating and interpreting data from multiple sources exacerbates this problem. Mining needs to catch up to industries such as oil and gas, which benefit from established data standards and service providers that streamline data integration and visualisation.
The Need for Advanced Visualization Tools
One of the most pressing issues is the need for advanced modelling software capable of visualising high-resolution mineralogical drill hole data. While current tools are adequate for analysing individual or small numbers of selected drill holes on a cross section, the mining industry needs software and systems capable of rapidly visualising, processing, interpreting and modelling thousands of drill holes with multivariate data. And this needs to happen in 3D where the drillholes are seen in spatial context, rather than in isolation or in core boxes. Then the interpretation and modelling will be transformed into a 3D mapping exercise between long, skinny outcrops, which are the drillholes. This would provide better understood resource models and support better decision-making.
- Geological Complexity and Limited Data
Ore deposits are inherently complex, often characterised by highly variable lithology, chemistry, mineralogy, and structural features. These complexities can make it difficult for geologists to develop accurate and easily updatable 3D models. Even with sophisticated modelling tools, there are always uncertainties due to the sparse nature of drill hole sampling, which provides only a limited view of the ore body extents.
Challenges with Drill Core Sampling
Drill core sampling remains the backbone of resource modelling, but it is costly and time-consuming. Sparse drill hole spacing leave gaps in data, which must be interpolated or extrapolated. This introduces a significant level of uncertainty, especially in deposits where mineralisation is erratic or affected by multiple and overlapping geological processes such as weathering, hydrothermal alteration, erosion and deformation. The farther the model strays from actual data points, the more unreliable it becomes.
- The Need for Multidisciplinary Integration
Modern ore body modelling requires integrating data from multiple scientific disciplines, including geophysics, geochemistry, and spectroscopy. However, merging these disparate data types can be challenging. Data sets vary in resolution and scale. Integrating them into a cohesive 3D model demands specialised knowledge and collaborative efforts across disciplines.
Interdisciplinary Collaboration as a Solution
The future of ore body knowledge lies in cross-disciplinary collaboration. By fostering greater communication between geoscientists and data scientists, and their downstream customers in mining planning and processing, mining companies can better reconcile differences in data quality, interpretations and assumptions. Such collaboration will lead to more accurate and reliable models, allowing companies to make more informed decisions.
- Technological Advancements and Limitations
The mining industry has seen a surge in new sensing technologies, such as automated core scanning and real-time downhole sensors. These technologies are revolutionising ore body knowledge by providing more detailed and timely data. However, their adoption has been slow, and significant technological gaps remain in real-time data acquisition and utilisation.
Real-Time Data for Decision-Making
One of the significant limitations in current practices is the time lag between data collection and decision-making. Geologists often rely on outdated data when making operational decisions, resulting in costly inefficiencies. By adopting real-time sensors and integrating machine learning algorithms for rapid data processing, the industry could bridge this gap and enable more precise and adaptive mining practices.
- Precision Mining and Resource Characterization
New advancements in core scanning technologies have illuminated the variability within ore bodies. With a better understanding of the extents and variability of mineralogical domains, mining companies can pursue precision mining approaches. This means targeting higher-value zones while minimising the overall operational footprint, which is particularly beneficial for highly variable precious and base metal deposits as well as bulk commodities.
Opportunities in Precision Mining
The shift from traditional mass mining methods to precision mining can yield significant economic and environmental benefits. By improving ore body knowledge and more selectively mining ore and waste, mining companies can reduce the amount of ore dilution, improving ore grade and minimising energy and water usage per ton of ore processed. This is especially critical as the industry faces increasing pressure to adopt more sustainable practices.
- Tailings Reprocessing and Legacy Mine Waste
Another emerging area in ore body knowledge is tailings and mine waste characterisation and reprocessing. As the industry develops a better understanding of legacy tailings and mine waste, they are discovering previously untapped opportunities to recover valuable minerals, many of which are critical for the energy transition. Geoscience Australia’s Atlas of Mine Waste, for example, has generated interest in reprocessing legacy tailings dams to extract value while reducing or eliminating environmental liabilities. Similarly, the MIWATCH (Mine Waste Transformation through Characterisation) team at The University of Queensland’s Sustainable Minerals Institute has been doing fantastic work in tailings and waste characterisation research for many years.
Characterising Mine Waste for Value Recovery
The challenge lies in characterising old tailings dams and developing processes that allow for economically viable mineral extraction while adhering to modern environmental standards. This requires a detailed understanding of the mineral composition and geochemical behaviour of tailings, which can vary significantly depending on the original style of in situ mineralisation and the subsequent mining and processing history.
- The Role of AI and Machine Learning in Future Exploration
Artificial intelligence and machine learning (AI/ML) are poised to transform ore body knowledge by processing vast amounts of data, identifying patterns, and generating more accurate predictive models. These technologies can help geologists predict undiscovered mineralisation, reduce exploration risks, and optimise real-time resource models.
Data-Driven Solutions for Reducing Uncertainty
With the huge amounts of new data available from sensing and scanning systems, AI and ML methods can improve the accuracy of grade estimation and ore body continuity predictions, significantly reducing uncertainty in resource modelling. While AI/ML is not a replacement for traditional geological expertise, these approaches provide powerful tools for enhancing ore body knowledge and supporting better decision-making across the mining lifecycle.
Conclusion
The challenges associated with ore body knowledge are multifaceted, encompassing data integration issues, geological complexity, technological limitations, and the need for interdisciplinary collaboration. Despite these challenges, real-time sensing, AI/ML, and precision mining advancements offer promising solutions. By embracing these innovations and fostering cross-disciplinary collaboration, the mining industry can achieve more accurate ore body models, enabling more efficient, sustainable, and economically viable mining operations.
Geologists play a pivotal role in this evolving landscape, acting as the stewards of ore body knowledge. Through innovative thinking and the adoption of advanced technologies, they can help close the gap between uncertainty and precision, ultimately shaping the future of mining.
A conversation with Paul Hodkiewicz
You mention that the mining industry lags others, like oil and gas, in data integration. What is the biggest barrier preventing mining companies from adopting better data standards and integration tools?
I think one reason is because tech service vendors and software companies in mining tend to work in isolation. There are no real equivalents to fully integrated oil and gas service providers like Schlumberger or Halliburton, for example.
How do you see advanced data visualisation tools evolving in the next five years to address the challenge of interpreting large volumes of geological data?
One advanced feature would be the ability to see and compare high-resolution drill hole data across scales, for example being able to map in 3D the detailed mineral textures in core scans across hundreds of drill holes across a deposit.
What are some practical steps mining companies can take to foster better communication and collaboration between geoscientists and downstream teams?
Aligning KPIs would be one step so that different disciplines across the mining value chain share the same goals. A big part of this is consistently focusing on the needs of your downstream customer in the value chain and seeing the mining operation as a complete system with a common purpose. These ‘mine-to-mill’ or ‘pit-to-port’ principles have been understood for a long time, so they are nothing new, but they are still not common practice in my experience. The opportunity to improve is still there.
Precision mining has the potential to reduce operational footprints and increase resource recovery significantly. What challenges are companies facing when implementing these methods, and how can they be addressed?
Trialling any precision or selective mining technologies would most likely be too disruptive in an existing operation, so companies need a ‘sandbox’ to play in. This would be a place to test and demonstrate potential, but also a place where it is OK to fail safely, learn from mistakes, and continuously advance technology developments. These could be smaller isolated orebodies or resources adjacent to existing operations. Another thing to consider is that to share the risks and rewards, this sandbox might have to be where several mining companies and service providers could collaborate in a joint venture-style arrangement. The Australian Automation and Robotics Precinct (AARP) near Perth is a great example of this sort of collaboration, where companies and vendors can trial and test new technology developments and workflows.
