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Authored by Min Liang, Senior Industry Process Consultant at GEOVIA, and José JoaquÃn González, Services Software Specialist.
This past year has witnessed rapid advancements in artificial intelligence (AI). It's clear that AI is becoming increasingly integrated into daily life around the world. You may already use an AI-powered car or rely on ChatGPT for research or writing, 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 enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. Unlike traditional programming, where explicit instructions are given, ML algorithms improve over time by learning from the data they process. This ability to learn from experience makes ML particularly effective in handling complex tasks.
In simple terms, ML allows computers to detect patterns in data without being explicitly programmed to do so. By analyzing these patterns, the system can approximate the underlying processes or models. This is achieved through supervised and/or unsupervised learning techniques.
**Supervised vs. Unsupervised Learning**
Supervised learning involves training a model using labeled data—data that includes both input and output pairs. The algorithm learns the relationship between inputs and outputs and uses this knowledge to make predictions on new data. For example, email providers use supervised learning to accurately identify spam messages.
Unsupervised learning, on the other hand, deals with unlabeled data. Instead of making predictions, it focuses on discovering hidden structures or patterns within the data. A well-known example is Netflix’s recommendation system, which suggests movies based on users' previous viewing behavior.
Depending on the data available and the specific task, one or both types of learning can be used. These algorithms can be combined to analyze diverse datasets efficiently, helping to interpret large volumes of information quickly and effectively.
However, it's important to remember that while ML models can process vast amounts of data rapidly, their accuracy heavily depends on the quality of the data and the effectiveness of the chosen algorithms.
**Machine Learning and Resource Modeling**
Resource modeling plays a critical role throughout the entire lifecycle of a mine—from discovery and evaluation to production, distribution, and rehabilitation. Accurate modeling is especially crucial during the early stages, as it directly impacts key decisions that shape the mining process.
To create a resource model, geologists collect a wide range of geoscience data, including geological records, topographic maps, field reports, borehole samples, satellite imagery, and more. They then develop a lithology model that separates data into estimation domains. From there, the model interpolates grades or other variables of interest in a block model, which becomes essential for mine planning.
While this sounds straightforward, resource modeling presents several challenges. Geologists must deal with large, complex datasets, invest significant time and resources in sample collection and testing, and account for multiple uncertainties.
With the help of AI, however, geologists can leverage ML to:
- Identify patterns in large volumes of data
- Develop automated processes for predicting key variables and quantifying risks
**Case Study 1: Machine Learning in Domain Separation**
One critical step in resource modeling is domain separation, especially when dealing with complex ore bodies. Geologists divide the deposit into distinct domains based on analysis of sample data, ensuring spatial continuity and statistical consistency.
We tested this approach on a porphyry-skarn deposit in Peru, using 32,711 composites from 273 drill holes. We applied a clustering algorithm that incorporated geostatistical Mahalanobis distance instead of Euclidean distance, allowing us to consider not only individual properties but also their relationships.
The results showed four distinct domains, each represented by a different color. Notably, the model identified transition layers between domains, highlighting high-grade zones and clearly defining boundaries.
Cluster 4 stood out due to its high average grade and maximum sample grade, indicating its economic significance. Using principal component analysis, we validated the clusters and found that integrating pseudo-samples increased confidence in domain identification, reducing the need for extensive manual work.
**Case Study 2: Machine Learning to Reduce Core Hole Drilling**
Core hole drilling provides essential data for resource estimation, but it is costly and time-consuming. Non-core holes are more efficient but lack the quality needed for certain minerals.
To address this, we explored using geophysical data to enhance spatial quality models and reduce reliance on core hole drilling. We used datasets from five Australian mines, combining core samples with geophysical measurements. Applying ML models like random forest and gradient boosting, we predicted lithology and quality in non-core holes.
Our results showed that using pseudo-samples alongside core data improved accuracy significantly. In some cases, we reduced core hole drilling by up to 28% without compromising estimation quality, demonstrating the potential of ML to cut costs and improve efficiency.
**Conclusion**
Machine learning excels at identifying complex patterns in multi-variate datasets, offering a deeper understanding of subsurface conditions. Its ability to process large volumes of data quickly and automate repetitive tasks streamlines workflows and supports faster, more informed decision-making.
By enhancing spatial feature analysis and generating pseudo-samples in areas with limited data, ML reduces dependency on expensive core hole drilling. This not only improves model confidence but also helps manage costs more effectively.
However, since resource estimation affects every stage of a mine’s lifecycle, it's crucial to apply ML with caution. While powerful, ML still requires validation by experienced geologists who understand the domain. When combined with industry expertise and cutting-edge technology, the mining sector can achieve greater efficiency and accuracy.
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