Discovering new sources of critical minerals is imperative to supply the materials needed for emerging technologies. However, traditional prospecting methods are costly and time-consuming. New artificial intelligence technologies offer more efficient ways to unlock these vital resources. This article, will explore how AI-enabled geospatial data analysis, automated mineral classification, and machine learning algorithms can accelerate critical mineral exploration and extraction. With the power of AI, companies can pinpoint drilling locations, streamline operations, and bring essential commodities to market faster than ever before. Continue reading to discover how implementing these innovative technologies give mining businesses a competitive edge in the critical minerals race.
The growing demand for critical minerals
Demand outstripping supply
The rapid expansion of technology and renewable energy has led to skyrocketing demand for critical minerals. According to estimates, demand for critical minerals is projected to more than double by 2030 under current climate pledges. The energy transition mineral market has already reached $320 billion in 2022 with rapid growth. Annual trade in energy-related critical minerals increased from $53 billion to $378 billion in 20 years. Reliance on Foreign Suppliers However, the supply of critical minerals has struggled to keep up with demand due to underinvestment in mining. This has led countries to become increasingly reliant on foreign suppliers, with China controlling much of the supply chain for minerals like rare earth elements that are essential for technologies like wind turbines, solar panels, and electric vehicles.
Using AI to unlock new deposits
To address supply shortages, mining companies are turning to advanced technologies like AI to help explore and assess mineral deposits more efficiently. Machine learning algorithms can analyze huge amounts of geological data to identify patterns that indicate the presence of critical minerals. These AI systems are able to explore and evaluate potential deposits at a scale and speed that humans alone could never achieve.
Partnerships to build resilient supply chains
Governments and companies are also forming strategic partnerships and alliances to build more resilient critical mineral supply chains. By diversifying sources and increasing recycling, countries can reduce their dependence on any single supplier. AI and automation will play a key role in creating circular supply chains that maximise the recovery of critical minerals.
With demand for technologies like electric vehicles and renewable energy only set to rise, critical minerals have become crucial for the transition to a sustainable future. AI and other advanced technologies can help unlock the supplies to make that future a reality.
How AI Is revolutionising mineral exploration
Reducing costs and improving efficiency
Artificial intelligence technologies can help reduce the costs and time associated with mineral exploration. AI algorithms have been used to analyse datasets and identify areas with a high probability of mineralization, allowing companies to avoid spending resources exploring areas unlikely to contain deposits. By focusing exploration on the most promising sites, AI enables more efficient use of funding and accelerates the discovery process.
Detecting patterns and anomalies
AI systems excel at detecting patterns in large, complex datasets that humans may miss. Machine learning algorithms can analyse volumes of geological, geochemical, and geophysical data to identify trends that could indicate the presence of mineral deposits. AI is also adept at spotting anomalies that stand out from the surrounding data, which could signify a mineralised zone. These capabilities allow AI to serve as an additional "set of eyes" to identify new exploration targets.
Evaluating Greenfield Sites
In unexplored "greenfield" areas, mineral exploration can be challenging due to the lack of existing data. AI technologies can help generate insights even where data is sparse. For example, AI can be used to analyse satellite imagery, drone data, and other remote sensing information to detect surface indicators of mineralization and prioritise sites for follow-up. AI can also extrapolate from known mineral deposits to identify geologically similar areas that may contain undiscovered resources. By leveraging these techniques, AI enables more systematic evaluation of greenfield sites.
AI will continue to transform mineral exploration, allowing companies to find deposits faster, at lower cost, and in previously unexplored regions. When combined with human expertise, AI can unlock access to the critical minerals needed for new technologies and sustainable economic growth. By partnering with AI, geologists and exploration teams gain a valuable new tool for making discoveries and securing supplies of the resources that power our future.
Case studies: AI success stories in mining critical minerals
Atomionics uses AI to locate copper, nickel, and zinc
Atomionics, a Singapore-based mineral exploration company, utilises AI and machine learning to explore for copper, nickel, and zinc deposits. Their AI models analyse vast geological datasets to identify areas most likely to contain mineral deposits. The company credits AI with improving the efficiency of mineral exploration and accelerating the discovery of new deposits.
VerAI Trains AI to Search for Lithium, Cobalt, and Gold
VerAI, an AI company focused on the mining industry, has developed machine learning models to explore for lithium, cobalt, and gold. The company's algorithms analyse satellite imagery, geochemical data, and geological maps to detect patterns that indicate the presence of mineral deposits. VerAI then uses the insights from their AI models to guide human geologists to promising locations for further exploration.
KoBold Metals, Backed by Bill Gates, Uses AI for Electric Vehicle Metals
KoBold Metals, a mineral exploration startup backed by Breakthrough Energy Ventures, founded by Bill Gates, is using AI to search for raw materials critical for electric vehicle batteries, such as cobalt, lithium, and nickel. KoBold's AI systems combine satellite imagery, geologic data, and physical samples to build 3D models of areas that may contain rich mineral deposits. The company believes that AI will be crucial to meeting the growing demand for raw materials that enable the transition to renewable energy and electric transportation.
The applications of AI and machine learning in the mining industry show significant promise for improving the discovery and extraction of critical minerals. As the demand for materials like lithium, cobalt, copper, and nickel continues to rise, advanced technologies can help locate new sources for these finite resources. AI has the potential to make the search for minerals more efficient, effective and environmentally friendly.
A bold future
Artificial intelligence holds tremendous promise for improving how we locate and extract critical minerals. By combining advanced machine learning with geological data, companies can pinpoint mineral deposits faster and more accurately. This enables us to unlock the materials needed for renewable energy infrastructure in a strategic, eco-friendly manner. Moving forward, the marriage of AI and earth sciences will only grow. You now have the foundational knowledge to follow these developments closely. Continue expanding your understanding of how AI can transform mineral exploration without compromising our planet. The opportunities for you to get involved with this work are endless.