Confidence at every stage – How AI is transforming the geological workflow
As the mining industry continues to evolve, so do the expectations placed on geologists.
While the geology itself hasn’t changed, the way decisions are made has. Geological modelling is now a more data-driven and less empirical process, shaped by larger datasets, tighter exploration budgets, and growing pressure to do more with less. Geologists are expected to deliver confident decisions faster and with fewer resources. In this environment, artificial intelligence (AI) - particularly machine learning and neural networks - has emerged as a powerful ally in navigating complexity and supporting better decision-making at every stage.
Traditional modelling methods, whether explicit or implicit, are often shaped by human assumptions. Explicit modelling can reinforce confirmation bias - geologists may build what they expect to see, based on prior knowledge or intuition. Implicit modelling offers more automation but still requires manual guidance, which can influence outcomes and limit the model’s objectivity. While expert judgment is a vital part of the process, it can also narrow the range of possibilities considered. Neural networks offer a different, independent approach. By learning directly from the data, without the need for predefined rules or guidance, they can reveal patterns and relationships that may not be immediately apparent. This enables geologists to build richer, faster, and more adaptable models that complement their expertise and expand their understanding of the deposit.
AI-powered geology tools that utilise neural networks, such as Micromine Origin Grade Copilot, are bridging the gap between subjective interpretation and data-driven decision-making. By supporting geologists across exploration, resource estimation, and production workflows, these tools are enabling a step-change in how geological data is interpreted and applied. This shift is already delivering tangible results in real-world projects across the entire geological workflow. In this article, we explore several key ways AI, in the form of neural networks, is enhancing geological modelling and empowering geologists to make more confident, informed decisions.
Exploration: Faster targeting, smarter decisions
In the early stages of exploration, decisions about where to drill and how to prioritise targets can make or break a project or organisation. Yet geologists are often working with inconsistent datasets – a mix of historical assays, incomplete logging, and limited structural data – while under pressure to deliver answers quickly. Traditional workflows, including implicit modelling, require significant manual input and deliver outcomes that are often qualitative in nature, such as grade shells, solids and surfaces, rather than quantitative grade models that clearly highlight where high-grade material is most likely to occur.
Neural networks offer a different approach. By training directly on drillhole assays, geochemistry, and spatial data, they can quickly predict where grades are likely to be concentrated and generate a fully quantified block model as output, all in a fraction of the time it would take to build a traditional model. This allows exploration teams to move from raw data to a testable hypothesis in minutes or hours, not days or weeks.
In a recent demonstration using Micromine Origin Grade Copilot on a publicly available gold exploration dataset, geologists simulated an early-stage drilling scenario with a mix of high- and low-grade intercepts dispersed across a broad area. By applying the neural network model trained on the assay data, it was possible to rapidly identify a consistent high-grade trend dipping southwest - a feature that wasn’t readily apparent from visual inspection alone. The model, generated in under a minute, not only highlighted the most prospective zones but also suggested new drilling directions that aligned with plausible structural controls. In contrast to an implicit grade shell approach, which offered only a generalised outline of mineralisation, the Grade Copilot model provided more precise guidance on where to allocate resources for follow-up drilling.
Figure 1: High-grade trend dipping southwest, identified using Micromine Origin Grade Copilot.
The benefits of this approach go beyond speed. Because neural networks produce numerical models, geologists can extract metrics like average grade and tonnage, directly from the output. These can be used to compare multiple targets side-by-side and prioritise those with the strongest economic potential. This is an especially useful capability when multiple prospects are competing for limited exploration budgets.
Geological modelling: Rapid interpretation with greater clarity
As projects progress from exploration into geological modelling and domaining, AI tools continue to offer advantages. Building reliable lithology or oxidation models has traditionally required significant manual setup including logging data coding, domain separation, and structural control interpretation. This process is not only time-intensive but highly dependent on the geologist’s individual interpretation, making it difficult to reproduce or validate.
In contrast, a neural network model can be trained directly on lithology logs, even those with inconsistent naming logging or repeated intervals downhole. Within minutes, the algorithm can classify categorical units across a block model, inferring trends and boundaries that match the underlying data without the need for prior domaining. Confidence values for each classification provide a transparent indication of model reliability, enabling geologists to interrogate ambiguous areas or adjust interpretations with far greater precision.
One illustrative scenario involves the challenge of modelling lithological units in a structurally complex deposit – a situation many geologists will recognise. In such cases, traditional implicit modelling often requires extensive re-coding of intervals and domain separation to account for repeated stratigraphy and faulted sequences. An AI-driven approach using neural networks, however, can handle these complexities more seamlessly. Without the need for predefined wireframes or intensive data preparation, the model can interpret lithological boundaries and identify structural trends directly from raw logging data.
In a simulated application, a neural network was able to define key geological contacts, including the intersection of a fault structure with multiple lithological units, while also highlighting areas of low classification confidence. These confidence maps provide geologists with a valuable diagnostic tool, helping to focus attention on ambiguous areas that may warrant closer inspection or data clean-up.
Figure 2: Lithology interpretation generated in minutes with Micromine Origin Grade Copilot.
This kind of output is vital to collaborative environments where different geologists may have varying interpretations of the same dataset. Rather than replacing human judgement, the AI provides a data-driven reference point that improves transparency, consistency, and trust in the modelling process.
Resource estimation: Independent validation and increased confidence
The role of AI becomes even more compelling in resource estimation. Estimate accuracy and defensibility are central to project valuation, yet traditional approaches often fall short in one key area: independent validation.
Estimates generated through ordinary kriging or inverse distance weighting typically rely on similar input assumptions, such as domains, search ellipses, and variogram models, which means cross-validations are often just reinforcing those same assumptions. The result is output that may appear consistent but offers a potentially false sense of confidence, built on the same underlying assumptions. AI tools provide a fresh lens by producing entirely independent estimates, free from those predefined inputs. This independence allows AI-driven models to serve as a robust benchmark, offering geologists a powerful (and rapid) way to validate traditional results and uncover insights that might otherwise be overlooked.
In a demonstration using an iron ore dataset, geologists investigated how AI could be used to validate a conventional estimation workflow. The initial model had been produced using standard techniques, including manual domaining and variogram-based interpolation, and following the typical steps that many resource geologists would recognise. To independently test the reliability of that estimate, the team applied a Micromine Origin Grade Copilot model to the same dataset, generating an alternative block model prediction.
Figure 3: Micromine Origin Grade Copilot model (left) compared with an Ordinary Kriging model (right), offering a rapid second opinion to support resource estimation confidence.
While the two models were broadly consistent in overall grade distribution, the AI-driven model provided a more nuanced view of local variability and revealed subtle geological patterns the traditional estimate had smoothed over. By comparing the two using swath plots and other visual diagnostics, geologists could evaluate how well the original model reflected the deposit’s complexity and quickly pinpoint areas where interpretation or domaining assumptions might need refining.
Crucially, this process didn’t require rebuilding the estimate from scratch. The AI model acted as a rapid, independent benchmark, produced in minutes, that either reinforced confidence in the conventional result or uncovered opportunities to improve it. In this case, the comparison did both: confirming much of the estimate while also directing attention to specific zones worth further analysis.
This kind of capability is a game-changer for projects operating under pressure. When time is tight, resources are limited, and decisions carry weight, geologists can’t always afford to rebuild multiple models from the ground up. Neural networks enable them to test scenarios rapidly – adjusting domain boundaries, orientations, or input variables – and immediately assess the impact. That means stronger models, faster insights, and greater confidence in the numbers presented to auditors, investors, and decision-makers. And it allows geologists to spend less time defending assumptions, and more time making high-value decisions.
Production: Real-time updates for responsive decision-making
In production environments, the need for speed and adaptability becomes even more critical. Geologists must constantly update models based on new grade control data, short-term drilling, or reconciliation results, often within tight planning cycles. Delays in updating models can translate directly into lost production opportunities or suboptimal extraction decisions.
Traditional modelling workflows are rarely fast enough to keep pace. AI, however, can ingest new data and rerun updated block models within minutes, ensuring geological models remain current and relevant to daily operations. Rather than reprocessing entire datasets manually or re-coding domains, production geologists can feed new assays directly into the existing AI model and generate an updated interpretation in near real-time.
This level of agility transforms operational decision-making. Teams can quickly assess whether a new zone is worth chasing, refine blast boundaries, or confirm whether a structural control observed in drilling should inform future stope designs. With AI handling the bulk of the modelling work, geologists are freed to spend more time on high-value analysis, interpreting new patterns, identifying emerging risks, or confidently guiding planning decisions.
Conclusion: Advancing geological modelling through AI
The integration of proven AI-modelling tools such as Micromine Origin Grade Copilot into geological modelling is not about replacing geologists, it’s about empowering them. AI offers a way to automate the heavy lifting of data processing and model generation, allowing geologists to focus on what they do best: interpreting the earth, challenging assumptions, and making decisions that shape the success of mining projects.
For those already adopting these methods, the benefits are clear; faster workflows, better-targeted exploration, more robust resource estimates, and higher confidence in operational decisions across the geological value chain. As geological data becomes more abundant and more complex, the role of AI will only grow in importance.
Now is the time for geologists to take the lead in shaping how AI is applied across the mining value chain. Not just as consumers of automated outputs, but as strategic thinkers who use AI to push the boundaries of what’s possible in geological science.
Want to see how AI is being applied in real-world geology?
Join the upcoming webinar, “How AI is Transforming Geology and Grade Modelling: A Conversation with Harmony Gold & Micromine,” where industry experts will explore practical case studies and share how AI is reshaping geological workflows, from interpretation to decision-making.