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Authored by GEOVIA’s Senior Industry Process Consultant, Min Liang and Services Software Specialist, José JoaquÃn González.
This past year has witnessed rapid advancements in artificial intelligence (AI), with AI becoming increasingly integrated into daily life around the world. You might already use AI-powered tools like self-driving cars or ChatGPT for research, but did you know that AI—specifically machine learning (ML)—is also a powerful tool for resource modeling?
Machine learning is a subset of AI that allows computers to learn from data without being explicitly programmed. It works by identifying patterns in data and using those patterns to make predictions or decisions. This process mimics how humans learn by repeating tasks and improving through experience.
In simpler terms, ML enables computers to detect patterns and trends in data without direct programming. These patterns help approximate an underlying model, which can be achieved through supervised or unsupervised learning techniques.
**Supervised vs. Unsupervised Learning**
Supervised learning involves training algorithms on labeled data, where both input and output are known. The algorithm learns the relationship between them and uses it to predict future outcomes. For example, email providers use supervised learning to identify spam.
Unsupervised learning, on the other hand, deals with unlabeled data. Instead of making predictions, it focuses on finding hidden structures or groupings within the data. Netflix recommendations are a good example, as they rely on users’ viewing history to suggest similar content.
Depending on the task and available data, either approach—or a combination of both—can be used to analyze complex datasets efficiently.
However, while ML models can process large volumes of data quickly, their accuracy heavily depends on data quality and the effectiveness of the chosen algorithms.
**Machine Learning in Resource Modeling**
Resource modeling is a critical component of mining operations, especially during the discovery, evaluation, and production phases. Accurate modeling at these stages is essential for making informed decisions that impact the entire mining lifecycle.
Geologists collect diverse geoscience data—including geological records, topographic maps, borehole samples, and satellite imagery—to create a comprehensive resource model. This model helps separate data into estimation domains and interpolate grades or other variables of interest, forming the basis for mine planning.
Despite its importance, resource modeling comes with challenges, such as handling large datasets, high costs of sample collection, and accounting for multiple uncertainties.
With the help of AI, geologists can now leverage machine learning to:
- Recognize patterns in vast datasets
- Automatically predict variables of interest and quantify risks
**Case Study 1: Machine Learning in Domain Separation**
One key challenge in resource modeling is defining distinct estimation domains, especially in complex ore bodies. A test case involving a porphyry-skarn deposit in Peru demonstrated how ML can improve this process.
Using 32,711 composites with 35 variables from 273 drill holes, we applied a clustering algorithm that considered cross-variograms among copper, gold, silver, and geochemical factors. This approach identified four distinct domains, including transition layers that traditional methods might miss.
The results showed improved confidence in domain identification, allowing for more accurate resource estimation and reducing the time required from weeks to minutes.
**Case Study 2: Reducing Core Hole Drilling with Machine Learning**
Core hole drilling is expensive and time-consuming, yet essential for resource estimation. In this study, we explored whether ML could reduce the need for core holes by leveraging geophysical data.
Using datasets from five Australian mines, we trained ML models to predict coal quality parameters such as ash, volatile matter, and relative density. By incorporating pseudo-samples generated by ML, we were able to reduce core hole drilling by up to 28% without compromising estimation accuracy.
**Conclusion**
Machine learning excels at analyzing complex, multi-variable datasets and uncovering patterns that may be missed by human analysts. It enhances the accuracy of subsurface modeling, speeds up workflows, and supports faster decision-making.
By combining ML with geostatistics, geologists can achieve more reliable resource estimates. Additionally, ML’s ability to generate pseudo-samples reduces reliance on costly core drilling, improving efficiency and cost management.
However, it's crucial to approach ML applications in resource modeling with care. While powerful, these tools still require expert validation. When combined with industry knowledge, AI has the potential to revolutionize the mining sector, driving greater efficiency and precision.
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