Montero advances AI innovation in mineral exploration

Bridging the gap between algorithms and field exploration

Montero Mining and Exploration integrated data from its Chilean exploration programs with artificial intelligence (AI) and machine learning to enhance mineral exploration. The company collaborated with US-based AI specialists to define AI's value in early-stage exploration, using geological insight and advanced data science to identify potential mineral targets.

Dr. Tony Harwood, CEO of Montero, said the company contributed to AI development for mineral exploration both technically and practically, bridging algorithm design and on-ground exploration.

"The data feedback from our projects is being used to refine AI model parameters and improve predictive accuracy for the wider mining sector," he said. Montero adopted an integrated data approach, combining field and regional data for AI and machine-learning analysis. These systems detected subtle patterns or anomalies linked to geological features and potential mineralization.

Montero's exploration team collected and validated high-quality field data through various methods at its projects. They sourced regional datasets from public geological databases, historical records, and satellite data in Chile.

Harwood explained that machine analysis enhanced geologists' abilities rather than replacing them. AI groups collaborated with Montero due to its openness to new technology and its geological experience with quality datasets.

"We provide a real-world testing ground in Chile's prospective mining belts," said Harwood. "The collaboration helps refine AI algorithms in complex geological terrains where pattern recognition is particularly challenging. It is a two-way exchange: we bring exploration field data and context, and our AI partners bring advanced modelling and computing capabilities."

Montero tested various AI models for different exploration data types. They combined specialized algorithms and tested unsupervised clustering algorithms to reveal unrecognized relationships in geological data.

This AI model integration gave Montero multi-dimensional predictive capability, linking various data into a coherent mineral-targeting framework.

Harwood explained that this approach resulted in more accurate, data-driven target prioritization for field programs and drilling strategies.

When selecting AI models, Montero defined the geological question first. They then chose or adapted models suited to specific data types. Geologists tested model predictions in the field, providing real-world feedback.

Harwood noted that AI had been effective in detecting various geological features and relationships. However, he cautioned about AI's complexity and the need for rigorous data cleaning and validation.

"However, AI also introduces complexity," said Harwood. "It requires rigorous data cleaning and strong geological validation to avoid false positives. The real gains are in speed and precision, generating target zones that can be verified in the field in days rather than weeks."

At Montero's projects, AI-driven modeling accelerated interpretation and highlighted overlooked features. Harwood emphasized how this collaboration turned raw data into discovery opportunities.

Montero's innovation lay in integrating diverse datasets into a single interpretive framework. Their geological team combined various data types to reveal hidden patterns and relationships.

Harwood stressed the importance of experience, discipline, and continuous learning in effective AI-driven exploration. He noted that many AI initiatives failed due to inconsistent or incomplete datasets.

For Montero, AI represented a logical progression in strengthening decision-making in exploration.

"It helps us operate more efficiently, reduce exploration risk, and make better choices about where to invest time and capital," said Harwood. "The aim is not to outsource discovery to technology but to use AI as a catalyst for faster, evidence-based exploration."