Geoscience Australia has deployed artificial intelligence systems that analyse decades of geological survey data to identify prospective areas for critical minerals including lithium, rare earth elements, and cobalt that are essential for renewable energy and electronics industries.

The AI systems integrate geological maps, geochemical surveys, geophysical data, and drilling results spanning over 50 years of exploration across Australia. Machine learning algorithms identify patterns associated with known mineral deposits, then extrapolate those patterns to unexplored areas.

Dr. James Johnson, who leads Geoscience Australia’s minerals systems research, said AI enables analysis at scales that weren’t previously practical. “We have petabytes of geological data, much of it collected before digital systems existed and never fully analysed. AI lets us find patterns in that historical data that point to exploration opportunities.”

Australia holds significant reserves of many critical minerals but much of the country remains under-explored. The challenge is that exploration is expensive and high-risk, with most exploration projects finding nothing economically viable.

Better targeting using AI could improve exploration success rates, reducing costs and accelerating discovery of deposits needed for clean energy transition. Demand for lithium, cobalt, rare earths, and other critical minerals is expected to increase dramatically over the next two decades.

The AI approach uses several machine learning techniques. Convolutional neural networks analyse spatial patterns in geological maps and satellite imagery. Gradient boosting models predict mineral potential based on combinations of geological, geochemical, and geophysical characteristics.

The models were trained on data from known mineral deposits, learning what geological conditions correlate with different deposit types. They can then scan unexplored areas and rank them by prospectivity.

One limitation is that AI models only learn from patterns in training data. They might miss entirely new deposit types or geological settings that differ from known examples. Human geologists provide oversight to ensure AI predictions are geologically reasonable.

Geoscience Australia is releasing prospectivity maps publicly, making them available to exploration companies and researchers. The maps don’t pinpoint specific deposits but identify regions worth further investigation.

Several junior exploration companies are using the AI-generated maps to guide their exploration programs. AI strategy support providers are also helping mining companies integrate AI prospectivity analysis with their existing exploration workflows and geological expertise.

One unexpected finding from the AI analysis is that many areas previously considered unpromising for particular mineral types show patterns similar to known deposits. Some of these areas were overlooked because they lack surface geological features that traditionally guide exploration.

For example, the AI identified several regions in South Australia and Western Australia with geological signatures similar to world-class lithium deposits but which haven’t been systematically explored for lithium. Several companies have since staked exploration tenements in those areas.

Whether AI predictions translate to actual discoveries won’t be known for several years. Mineral exploration typically takes 5-10 years from initial targeting to resource definition, assuming discoveries are made.

The AI systems are particularly useful for targeting buried deposits where surface geology provides few clues. Advanced geophysical methods like magnetotellurics and gravity surveys can detect deep structures, and AI helps interpret that data in the context of broader geological patterns.

Rare earth elements represent a particularly important exploration target. China currently dominates rare earth production and processing, creating supply chain vulnerabilities for countries like Australia that depend on rare earths for technology manufacturing.

Australia has several known rare earth deposits including Mount Weld in Western Australia, but developing new sources requires finding additional deposits and building processing capabilities. AI-guided exploration could accelerate deposit discovery.

The project cost about $8 million over three years, funded through Geoscience Australia’s regular appropriation. That’s modest compared to the billions spent on mineral exploration annually, and if it improves exploration success rates even marginally, it provides enormous economic value.

Critics note that AI for mineral exploration is being developed by several companies globally, and Geoscience Australia’s approach might not be uniquely superior. But making results publicly available ensures all Australian explorers benefit, not just those who can afford commercial AI services.

The public good aspect is important. Geoscience Australia’s role is supporting national economic development, which means ensuring exploration knowledge benefits Australian industry broadly rather than just a few companies.

Partnerships with state geological surveys are extending the AI prospectivity mapping to cover Australia systematically. Each state has unique geological characteristics and data coverage, requiring adaptation of models and analysis approaches.

The AI systems are also being applied to other resources including groundwater, geothermal energy, and hydrogen storage sites. The fundamental approach of integrating diverse datasets to predict resource occurrence applies across many domains.

One technical challenge is integrating data at different scales and resolutions. Some geological data covers entire regions at coarse resolution, while other data covers small areas in detail. Getting AI models to handle that multi-scale integration effectively requires careful design.

The project includes extensive validation where AI predictions are compared against known deposits that were excluded from training data. This tests whether models genuinely predict prospectivity or just memorise training examples.

Early validation results are encouraging, with AI correctly identifying about 70-75% of known deposits in hold-out test sets. That’s substantially better than random exploration, though not perfect.

The prospectivity maps are already influencing exploration decisions. The Australian Exploration Geoscience Conference in September 2025 featured multiple presentations from companies using the AI-generated data for targeting.

Whether this leads to significant new mineral discoveries remains to be seen. But at minimum, it demonstrates how AI and decades of public geological data investment can combine to support economic development and resource security.

Geoscience Australia plans to update prospectivity maps annually as new exploration data becomes available and as AI models improve. This creates a continuously improving resource for exploration targeting that incorporates the latest knowledge and technology.