Using AI to get from explore to ore

Artificial intelligence is rapidly transforming mineral exploration, creating a technological revolution that promises to slash the time between initial discovery and commercial mining operations.

Industry experts are increasingly turning to AI-powered systems to analyze vast quantities of geological data, helping mining companies more efficiently identify promising deposits and accelerate the traditional exploration-to-production timeline.

“The exploration process has historically been extremely time-consuming and expensive,” explains Dr. Sarah Chen, chief geologist at MineTech Solutions. “Companies would spend years collecting and analyzing samples, often with limited success. AI is changing that equation dramatically.”

The transformation comes at a critical time for the mining sector. With global demand for critical minerals like copper, lithium, and rare earth elements surging due to clean energy transitions and technological advancement, pressure is mounting to bring new resources online faster than ever before.

Rio Tinto, one of the world’s largest mining corporations, recently deployed machine learning algorithms across its exploration portfolio in Western Australia. The system processes data from drill cores, satellite imagery, and historical geological surveys to identify patterns that human geologists might miss. Company officials report that the AI system has already identified three potentially viable copper deposits that conventional methods had overlooked.

“We’re seeing a 40 percent reduction in early-stage exploration time,” says Marcus Williams, Rio Tinto’s head of digital innovation. “What used to take five years can now be accomplished in three, and with greater confidence in the results.”

Similar initiatives are underway at BHP and Anglo American, with both companies investing heavily in AI capabilities. BHP recently established a dedicated Advanced Analytics Centre in Singapore, focused specifically on applying machine learning to exploration activities across its global operations.

The financial implications are substantial. Industry analysts estimate that AI-assisted exploration could reduce overall costs by 20-30 percent while simultaneously improving discovery rates. For junior mining companies with limited capital, these technologies offer a potential lifeline in the high-risk exploration business.

Vancouver-based Quantum Minerals, a mid-tier exploration company, credits AI systems for its recent lithium discovery in Nevada. “We fed historical survey data, satellite imagery, and regional geological information into our machine learning platform,” explains Quantum CEO Diana Roberts. “The AI identified anomalies that suggested a lithium deposit where conventional wisdom said we wouldn’t find anything. Six months later, our drill results confirmed it.”

The technology extends beyond just finding deposits. AI systems are increasingly being deployed to optimize drilling programs, reducing the number of holes needed to characterize an orebody. This translates to lower environmental impact and reduced exploration costs.

Specialists in the field caution, however, that AI remains a tool rather than a replacement for geological expertise. “The algorithms are only as good as the data they’re trained on,” notes Dr. James Morley, professor of economic geology at Colorado School of Mines. “Human geologists still need to validate findings and apply contextual understanding that machines currently lack.”

Environmental groups have expressed mixed reactions. Some see AI-assisted exploration as potentially reducing the footprint of mining activities by allowing more targeted operations. Others worry that accelerated discovery could lead to more rapid resource extraction without adequate safeguards.

“Efficiency is good, but not at the expense of proper environmental assessment,” says Elena Garcia of the Resource Sustainability Coalition. “If AI helps companies find deposits faster, regulatory frameworks need to ensure that environmental protections keep pace.”

For countries with resource-dependent economies, the AI revolution presents both opportunities and challenges. Nations like Chile, Australia, and Canada are investing in digital mining initiatives to maintain their competitive edge, while developing countries worry about being left behind in the technological race.

The Peruvian government recently announced a partnership with IBM to develop AI capabilities for its mining sector, with a focus on training local engineers and geologists. “We cannot afford to miss this technological wave,” stated Peruvian Mining Minister Carlos Mendoza. “Our mineral wealth must benefit from the most advanced tools available.”

Investors are taking notice too. Venture capital funding for AI mining startups reached $780 million in 2022, nearly triple the amount from two years earlier. Companies specializing in geological machine learning, like Toronto-based OreFinder and Australia’s GeoAI, have seen their valuations soar.

As the technology matures, industry observers predict further compression of the mine development timeline. “Within five years, we could see the exploration-to-production cycle reduced by half in certain commodities,” predicts mining analyst Jennifer Wu of Global Resources Capital. “For critical minerals essential to the energy transition, that acceleration could prove crucial to meeting global demand.”

For an industry often characterized as conservative and slow to change, the embrace of artificial intelligence represents a significant shift in mindset. Whether this digital transformation delivers on its promise remains to be seen, but one thing is clear: the race from exploration to ore has entered a new, accelerated phase.