How AI is reshaping early-stage mineral exploration

Advancing prediction 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 its exploration sites in Chile. The company is working with US-based AI specialists to refine AI model parameters and improve predictive accuracy for the wider mining sector.

Dr Tony Harwood, CEO of Montero, says the joint initiative is refining AI algorithms in complex geological terrains where pattern recognition is particularly challenging.

“The collaboration is a two-way exchange in which we bring exploration field data and context, and our AI partners bring advanced modelling and computing capabilities,” he says.

Montero integrates field and regional data into unified datasets for analysis by AI and machine-learning models. These technologies can detect subtle patterns or anomalies associated with geological features and indicators of potential mineralisation.

Harwood explains that machine analysis does not replace the company’s geologists but significantly enhances their ability to investigate beyond conventional boundaries and prioritise 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 on-the-ground exploration,” he says.

Testing and selecting AI models

Montero is testing a suite of complementary AI models designed to process different types of exploration data.

“A single model cannot interpret geology, so we combine specialised algorithms, each contributing unique strengths to the predictive process,” says Harwood.

The integration gives Montero a multi-dimensional predictive capability, linking surface geochemical patterns, key structural features in the rock, and spectral signals from satellite data into a coherent framework for targeting minerals.

“The result is a more accurate, data-driven prioritisation of exploration targets that guides the company’s field programme and drilling strategy,” he adds.

To select the appropriate machine-learning models, Montero first defines the geological question – such as identifying porphyry-style alteration or detecting specific geochemical signatures. The AI teams then choose or adapt models suited to the data type, such as convolutional neural networks for image-based datasets and gradient-boosting algorithms for numerical geochemical data.

The exploration team trains and validates models using known deposits as analogues. They test the models’ predictions in the field, closing the feedback loop between machine output and real-world results.

Insights and impact

Harwood says early AI applications have been most effective in detecting geochemical anomalies — the unusual chemical signatures found in rock — and in mapping alteration zones using remote-sensing data. The AI models have identified subtle spatial relationships between datasets, including faint geochemical trends and structural patterns that manual analysis might have overlooked.

But AI also introduces complexity. Harwood cautions that 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,” he says.

At the company’s Elvira project in the Maricunga mining belt in northern Chile, AI-driven data integration has highlighted previously overlooked alteration zones, which Montero has since confirmed through field sampling. These advances demonstrate how multidisciplinary collaboration can turn raw data into meaningful discovery opportunities.

For Montero, AI is the logical next step in strengthening decision-making. “It improves efficiency, reduces risk, and sharpens how we deploy time and capital,” says Harwood.

“The goal isn’t to outsource discovery, but to use AI to accelerate evidence-based exploration.”

Harwood emphasises that AI’s value lies in its ability to rapidly test geological hypotheses through advanced pattern recognition and cross-validation. This enables Montero to generate, rank, and refine exploration targets at a pace that traditional methods cannot match — a shift that is reshaping expectations for early-stage discovery.

Author: Dr Harwood, Montero's CEO.

Read the article online at: https://www.globalminingreview.com/mining/12122025/how-ai-is-reshaping-early-stage-mineral-exploration/