Marine Gre Plastic Pipe,Hot Fusion Plastic Pipe,Large Diamater Gre Pipe,Fiberglass Reinforced Pipe,GRE plastic pipe Jingjiang Haihong Plastic & Rubeer Science -Technology Co.,Ltd , https://www.marineplasticpipe.com
How AI can Enhance your Resource Modeling
Authored by Min Liang, Senior Industry Process Consultant at GEOVIA, and José JoaquÃn González, Services Software Specialist.
This past year has witnessed an explosive evolution in artificial intelligence (AI). It's evident that AI is rapidly 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, particularly machine learning (ML), is also a powerful tool in resource modeling?
Machine learning is a subset of AI that uses data and algorithms to mimic how humans learn—by performing tasks repeatedly and improving through experience. In simpler terms, ML allows computers to identify patterns and trends in data without being explicitly programmed to do so. These patterns help approximate underlying processes or models using supervised and/or unsupervised learning techniques.
Supervised learning involves training machines on labeled data—data with known inputs and outputs. The algorithm learns from this data to create a function capable of predicting future outcomes. The goal is to minimize the difference between predicted and actual results. Spam filters are a classic example of this approach.
In contrast, unsupervised learning doesn’t require labeled data. Instead, it focuses on uncovering hidden structures or patterns within the data. For instance, Netflix uses unsupervised learning to recommend movies based on your viewing history.
Depending on the task and available data, one or both methods can be used. Algorithms can also work together to detect patterns across diverse datasets, making the analysis of large volumes of information much more efficient.
It’s important to note that while ML models can process vast amounts of data quickly, their accuracy depends heavily on data quality and the effectiveness of the chosen algorithms.
Throughout the lifecycle of a mine—from discovery and evaluation to production, distribution, and rehabilitation—resource modeling plays a crucial role, especially during the early stages. Accurate modeling at these points is essential for making decisions that impact the entire mining process.
To build a resource model, geologists gather data from various sources such as geological surveys, topographic maps, field reports, borehole samples, satellite imagery, and magnetic readings. They then create a lithology model, divide the data into estimation domains, and interpolate grades or other variables into a block model. This block model serves as a foundation for mine planning.
However, resource modeling comes with challenges, including handling large and complex datasets, the time and cost of sample collection, and managing multiple uncertainties.
With AI, geologists can leverage machine learning to:
- Identify patterns in massive datasets
- Develop automated prediction models and quantify potential risks
Case Study 1: Machine Learning in Domain Separation
One critical step in resource modeling is domain separation, especially for complex ore bodies. Geologists typically divide the deposit into distinct domains based on sample data and apply specific parameters to ensure spatial continuity.
We tested this with a porphyry-skarn deposit in Peru, using 32,711 composites with 35 variables from 273 drill holes. We applied a clustering algorithm that used the Mahalanobis distance instead of Euclidean distance, incorporating cross-variograms among copper, gold, silver, and geochemical factors.
The result was four distinct domains, each shown in a different color. Unlike traditional methods, this approach identified transition layers and accurately pinpointed high-grade zones.
Table 1 shows cluster 4 has a higher average grade and maximum sample grade, indicating its significance.
We validated the clusters using principal component analysis and found that integrating pseudo-samples increased economic grade by up to 1% and reduced modeling time significantly.
Case Study 2: Reducing Core Hole Drilling with Machine Learning
Core hole drilling provides essential data but is expensive and time-consuming. Non-core holes are cheaper but don’t offer the same quality samples. To address this, we used geophysical data and ML models to predict coal quality in non-core holes.
Using data from five Australian mines, we trained models like random forest and gradient boosting to predict ash content, volatile matter, and other properties. We created pseudo-samples and compared estimations using core samples only, all samples, and varying percentages of core samples with pseudo-samples.
Results showed that predictions were highly accurate, with MAE below 10% and R² above 0.9. We achieved a 20–28% reduction in core hole drilling without compromising accuracy.
Conclusion
Machine learning excels at analyzing complex, multi-variable datasets and identifying patterns that may go unnoticed by humans. By revealing variations in geological data, it helps better reflect subsurface reality. Its ability to process large datasets quickly and automate repetitive tasks not only streamlines workflows but also supports faster, more informed decision-making.
By focusing on spatial features and combining with geostatistics, ML enhances the confidence and reliability of resource estimates. Additionally, generating pseudo-samples reduces reliance on core hole drilling, lowering costs and improving efficiency.
However, since resource estimation affects the entire mine lifecycle, it’s vital to approach ML applications carefully. Even with advanced capabilities, experienced geologists are still needed to validate results. When combined with industry knowledge and cutting-edge technology, the mining sector can enter a new era of efficiency and accuracy.
Join the GEOVIA User Community to connect with experts, ask questions, and share insights. Whether you're a beginner or an expert, this is the place to learn, engage, and shape the future of mining. Sign up today—it’s free!
Next Article
Why Your Garage Door Is Making Those Strange Noises
Prev Article
How AI can Enhance your Resource Modeling