How to improve critical mineral discovery with AI

Due to their range of applications in the production of energy transition technologies, critical minerals will be among the most important and desired mined materials over the next 20 years.

The International Energy Agency (IEA) notes that critical minerals experienced strong demand growth in 2023, with lithium demand rising by 30%, and nickel, cobalt, graphite and rare-earth elements all seeing increases ranging from 8% to 15%. The mining industry therefore faces the historically significant challenge of providing the supply necessary to avoid bottlenecks and destabilising price spikes. Will traditional discovery methods be sufficient to unearth the vital deposits?

Mining Technology sat down with Lorraine Godwin, vice-president of VerAI Discoveries, to discuss how AI can be used to accelerate the detection of economically significant critical mineral deposits.

Lorraine Godwin:​​​​​​​ The demand for critical minerals continues to go up but at the same time the industry’s ability to create that new supply is not keeping up. Based on the latest figures released by the IEA, the demand gap for critical minerals such as copper is just going to keep growing through to 2050, while the number of new copper discoveries continues to fall. 

Under the current scenario, the industry is never going to meet the word’s critical mineral supply needs. Many of the low hanging fruit discoveries have been made, the deposits near the surface, but how are covered deposits going to be found? This is the problem VerAI is trying to solve with AI. 

We search for economic deposits, not just mineralisation, in the vast covered areas that traditional methods cannot see through. We are using AI in a different approach to discovery and to revolutionise the way we can predict the location of the next big deposits.

Lorraine Godwin:​​​​​​​ The industry currently accepts that the ratio of fully developed mines to exploration projects that are drilled is 1:1,000. We are trying to change this ratio. Also, the time it takes to get from initial exploration to drilling and discovery can be quite a few years. We are changing this as well. We are trying to derisk the early-stage discovery process by improving the odds that more of these projects get to the mine development stage – our goal is ultimately one in ten or less. We are also significantly shortening the time to the drilling stage. 

Traditionally, in the discovery process there is a lot of subjectivity. I have seen two geologists with two very different hypotheses about what is going on below the surface, and it is all based on their knowledge and past experience. You can have very open-ended hypotheses in areas where geologists can see evidence, and none at all at the vast covered areas where we are focusing our searches. 

What we are trying to do is take a much more systematic approach by using data that is objective and using AI to find the concealed deposits in the data, that our eyes just cannot see, or that is outside the grasp of expert knowledge. We use geophysics data. For us it is not about a big data approach, which is what some other AI companies are doing to approach this problem. They are using all different kinds of data, but the problem with this is that the data can be sporadic, intermittent, irrelevant for the problem or heavily biased. 

We have done a lot of different types of validation, including blind testing in North America and South America on different commodity types. The way we did blind testing was training the AI on known economic deposits or advanced projects in the known area, and then instructing it to look in the adjoining areas. 

For example, in Arizona we built a profile on a very large known copper deposit, and then we trained the AI to find similar patterns. We generated a few targets and one of the targets was a known economic deposit that we did not tell the AI was there.

Lorraine Godwin: We have successfully tested it on several different mineral types including copper, gold, lithium, nickel, silver and zinc, and we are now working with US Critical Minerals on a rare earth elements project.

Lorraine Godwin: We are not looking to be a fee-for-service type business, nor do we want to be the explorers or operators of mines. We focus on early-stage discovery of multiple projects in multiple jurisdictions, and we create a portfolio of mineral assets. That is what we are good at. We then partner with explorers and financiers to develop these assets. We believe we improve the odds of prediction, so that we can help our partners know where to start, finding that needle in the haystack, and then they come in and do what they do best as they develop and advance the project. We are looking for exposure to the upside if our AI targets lead to a discovery, through equity and royalty in our projects.

Lorraine Godwin: We have generated our own portfolio of AI projects. If the land in which we have made the discoveries is open and free, we stake those claims and then look to bring partners in. In other cases, we are getting approached by partners with their own land, or those who are interested in establishing a strategic alliance before any target was generated.

Lorraine Godwin: You do not have to do as much site investigation work which can cause ground disturbance, high costs and potential environmental impact. We are trying to reduce that as if we tell you where to start, and then enable very high drill precision, you do not have to put as many drill-holes in the ground over years to reach a discovery.

Lorraine Godwin: We have now deployed our technology in Canada, the US, Mexico, Chile and Peru. We are open to engage with partners on new jurisdictions in the Americas and potentially Australia in the future. Australia is a classic example of where a lot of the easy deposits have been found and the next ones will be under areas of concealment.

Lorraine Godwin: Yes, there are competitors, but there are many differences in the way they use AI and mainly in our business models. One of the differences is that most are taking a big data approach. The challenge with this is that you can bring bias into your data model. We find it better to work only with data relevant for the problem; working as closely to the original data as it is collected from the relevant instrument as possible. You remove a lot of human and natural bias that could weaken your AI model this way.

Lorraine Godwin: I think it is already happening. At mining conferences, AI is at the forefront of most keynote speeches. Major mining companies are all rushing to develop or invest in this technology, whether they are working in house or trying to partner with groups like VerAI to advance the technology quickly. They see how AI is impacting other industries like the medical industry, pharma or financial services, and how it is changing the game very quickly. The problems we are trying to solve with pinpointing location of concealed objects are very similar to the challenges some other industries are solving today with AI.

Lorraine Godwin: I think in mining AI will help speed up early-stage discovery, and it will help geoscience experts focus their time and effort. So, I think it shifts where we apply our expertise, but it does not replace people in terms of labour. AI cannot operate in isolation either. It needs the geo experts, the people that understand the rocks and the geology and how to turn [a discovery] into a mine. AI is just going to allow people to [better] focus their time and effort.