How AI can Enhance your Resource Modeling

How AI can Enhance your Resource Modeling

Authored by Min Liang, Senior Industry Process Consultant at GEOVIA, and José Joaquín González, Services Software Specialist. This past year has seen incredible progress in the development of artificial intelligence (AI). It’s clear that AI is quickly becoming a part of everyday life around the world. You might 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 branch of AI that allows systems to learn from data, identifying patterns and making decisions with minimal human intervention. Just like humans improve through experience, ML algorithms refine their performance by analyzing data repeatedly. In simple terms, ML enables computers to detect patterns and trends in data without being explicitly programmed. The system then uses these patterns to approximate underlying processes or models, using either supervised or unsupervised learning techniques. ### Supervised vs. Unsupervised Learning Supervised learning involves training models using labeled data—data that includes both input and output pairs. The algorithm learns the relationship between inputs and outputs to make predictions for new data. This approach is commonly used in spam filters, where the system learns to identify unwanted messages based on previous examples. Unsupervised learning, on the other hand, works with unlabeled data. Instead of predicting outcomes, it focuses on discovering hidden structures or groupings within the data. A great example is how Netflix recommends movies based on your viewing history, without needing explicit labels. Depending on the task and the data available, one or both approaches can be used. Combining them allows for more accurate pattern recognition across diverse datasets, improving efficiency in handling large volumes of information. It's important to remember that while ML models can process vast amounts of data quickly, their accuracy depends heavily 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, especially during the early stages of discovery and evaluation. Accurate modeling is essential for making informed decisions that impact the entire mining process. Geologists gather various types of geoscience data, such as geological reports, topographic maps, borehole samples, and satellite imagery. Using this data, they create a detailed model of the rock layers (lithology) and estimate mineral grades. This block model becomes the foundation for mine planning. However, resource modeling comes with its own set of challenges, including dealing with complex datasets, the high cost of sample collection, and managing multiple uncertainties. With the help of AI, geologists can leverage machine learning to: - Identify patterns in massive datasets - Automatically predict key variables and assess potential risks ### Case Study 1: Machine Learning in Domain Separation A key step in resource modeling is defining distinct estimation domains, especially for complex ore bodies. This involves analyzing sample data and applying statistical methods to ensure each domain is spatially consistent. We tested a porphyry-skarn deposit in Peru using 32,711 composites with 35 variables. A clustering algorithm was employed, using the Mahalanobis distance instead of Euclidean distance. This allowed the model to account for relationships between copper, gold, silver, and geochemical properties. The results showed four distinct domains, with clear boundaries identified. Cluster 4 had the highest average grade, demonstrating the model's ability to pinpoint high-grade zones. Principal component analysis confirmed the validity of the clusters, leading to a 1% increase in economic grade within the main domain. The process, which traditionally takes weeks, was completed in minutes. ### Case Study 2: Reducing Core Hole Drilling with ML Core hole drilling provides essential data but is expensive and time-consuming. Non-core holes are cheaper but often lack quality samples for certain minerals. We tested ML models—including random forest, gradient boosting, and deep learning—to predict coal quality using geophysical data. By combining core samples with ML-generated pseudo samples, we achieved high accuracy in lithology prediction and coal quality estimation. Results showed that reducing core drilling by up to 28% could maintain the same level of accuracy. This not only lowers costs but also improves efficiency in resource modeling. ### Conclusion Machine learning excels at processing complex, multi-variable datasets, uncovering patterns that may be missed by human analysts. It enhances our understanding of subsurface conditions and streamlines workflows, enabling faster and more informed decisions. By integrating ML with geostatistics, we can improve the confidence and reliability of resource estimates. Additionally, ML’s ability to generate pseudo samples reduces the need for costly core drilling, making the process more efficient. However, it's crucial to approach ML applications with care, as resource estimation impacts all stages of a mine’s lifecycle. Geologists with domain expertise remain essential for validating results. When combined with industry knowledge and cutting-edge technology, ML has the potential to transform the mining sector, ushering in a new era of efficiency and precision. --- Join the GEOVIA User Community to connect with experts, share insights, and stay updated on the latest developments in mining technology. Whether you're a beginner or an experienced professional, this platform offers valuable resources for learning and networking. Create a free account today and be part of a growing community shaping the future of sustainable mining.

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