Montero Mining is playing a leading role in defining the application of AI in early-stage mineral exploration by providing Chile with a real-world testing ground for advanced technologies.
Forecasting advances in mineral exploration
Montero Mining and Exploration is playing a leading role in defining the application of AI in early-stage mineral exploration by providing a real-world testing ground for advanced technologies at exploration sites in Chile. The company is working with U.S.-based AI experts to refine the parameters of its AI models to improve predictive accuracy for the broader mining sector.
Montero CEO Dr. Tony Harwood says the joint initiative is to improve AI algorithms in complex terrain where pattern recognition is particularly difficult.
“This collaboration is a two-way street, with us providing exploration site data and context, and our AI partner providing advanced modeling and computing capabilities,” he says.
Montero combines field and regional data into unified datasets for analysis with AI and machine learning models. These techniques can detect subtle patterns and anomalies associated with geological features and indicators of potential mineralization.
Harwood explains that mechanical analysis does not replace the company's geologists, but greatly enhances its ability to probe beyond traditional limits and prioritize mineral targets more efficiently.
“Our contribution to the development of AI for mineral exploration is both technical and practical, helping to bridge the gap between algorithm design and surface exploration,” he says.
Testing and selecting AI models
Montero is testing a set of complementary AI models designed to process different types of exploration data.
“No single model can interpret the geology, so we combine specialized algorithms, each offering unique strengths to the prediction process,” says Harwood.
This integration gives Montero multidimensional predictive capabilities, linking surface geochemical patterns, key structural features of rocks, and spectral signals from satellite data into a consistent framework for targeting minerals.
“The result is more accurate and data-driven exploration target prioritization, which guides the company's field program and drilling strategy,” he added.
To choose the right machine learning model, Montero first defines the geological problem, such as identifying porphyry alteration or detecting specific geochemical signatures. The AI team then selects or adapts the appropriate model for the data type, such as a convolutional neural network for image-based datasets or a gradient boosting algorithm for numerical geochemistry data.
The exploration team trains and validates the model using known deposits as analogs. They test the model's predictions in the field, closing the feedback loop between the machine's output and real-world results.
Insight and impact
Harwood said early AI applications were most effective at detecting geochemical anomalies (abnormal chemical signatures found in rocks) and mapping alteration zones using remote sensing data. The AI model identified subtle spatial relationships between datasets, including subtle geochemical trends and structural patterns that might have been missed by manual analysis.
But AI also brings complexity. Harwood cautioned that rigorous data cleaning and strong geological validation are needed to avoid false positives.
“The real advantage is in speed and accuracy, producing target zones that can be verified in the field in days rather than weeks,” he says.
At the company's Elvira project in northern Chile's Maricunga mining region, AI-powered data integration revealed previously overlooked alteration zones, which Montero subsequently confirmed through field sampling. These advances demonstrate how multidisciplinary collaboration can turn raw data into opportunities for meaningful discovery.
For Montero, AI is the logical next step to enhance decision-making. “It increases efficiency, reduces risk, and provides clarity on how to allocate time and capital,” says Harwood.
“The goal is not to outsource discovery, but to use AI to accelerate evidence-based exploration.”
Harwood emphasizes that the value of AI lies in its ability to quickly test geological hypotheses through advanced pattern recognition and cross-validation. This allows Montero to generate, rank, and refine exploration targets at a pace unattainable through traditional methods, a change that reshapes expectations for early-stage discovery.
Author: Dr. Harwood, CEO of Montero.
The article is available online: https://www.globalminingreview.com/mining/12122025/how-ai-is-reshaping-early-stage-mineral-exploration/
