In recent weeks, every day that the weather permitted, a helicopter contracted by KoBold Metals flew over a remote part of northern Quebec carrying some unusual cargo. 

A 115-foot-wide copper coil dangled from the belly of the craft, sending electromagnetic waves into the earth and creating currents in rocks deep underground. Any good electrical conductors sent telltale signals back to a receiver coil, suggesting that the rocks might contain valuable deposits of nickel and cobalt—metals used in the batteries powering cell phones, laptops, and electric cars.

After the pilot finished scanning a swath of land—on a good day, the helicopter will cover more than 100 miles—the data was transmitted via satellite to KoBold scientists working in offices thousands of miles away. These researchers plugged the new survey data into machine-learning models, which combined it with reams of other data the company has gathered to improve understanding of the region’s geology. Finally, they fed all this information into an artificial-intelligence system KoBold developed in partnership with Stanford University. The system draws on vast computational power to advise the team on the best places to survey next.

Using this high-tech array of software tools, the San Francisco–based mineral exploration company, backed by Bill Gates and Jeff Bezos, can change its airborne survey plans from day to day to more quickly home in on promising places to drill.

It’s a far cry from how geologists have traditionally hunted for mineral deposits, which amounts to collecting field data and analyzing it when the exploration season is over. 

Where conventional methods relied wholly on human interpretation, these days data science and machine learning are becoming a bigger part of the effort to find the next big payday underground. Recognizing that the metals underpinning modern technology are getting harder to find as the clean-energy sector’s appetite for them grows exponentially, Silicon Valley investors are betting that companies like KoBold can help the mining business keep up, accelerating the discovery of new ores and driving down its costs. 

Whether or not that hunch is correct, experts say the involvement of major tech figures could draw attention to the lack of investment in new mines and potentially attract needed funding for startups hoping to harvest metals in a more environmentally and socially responsible way.

“When people think about electric vehicles, it’s easy to forget about the raw materials that feed into this shiny stuff we see in the showroom,” says Kwasi Ampofo, a mining-sector analyst at the energy research firm BloombergNEF.

Harder to find

Demand for metals and minerals like lithium, cobalt, graphite, and nickel, all used in batteries powering electric vehicles and the grid, is expected to surge in the coming years. A May report by the International Energy Agency found that deploying clean-energy technologies at the pace needed to prevent 2 ˚C of global warming will increase demand for minerals used in energy storage more than thirtyfold by 2040.

But the mining sector isn’t keeping pace. It can take more than a decade to bring new mines online once a company secures mineral rights and permits. Discovering the best place to dig a hole in the ground can take even longer: with most of the easily identifiable, high-grade ore bodies already found and investment in exploration declining, new mineral deposits are getting harder to spot. A commonly stated rule of thumb in the field is that only one out of 100 sites evaluated will turn up a deposit that can profitably be mined. But some experts think it’s closer to one out of 1,000.

Data science tools like machine learning, in which algorithms are trained to sift through massive data sets and spot patterns, have the potential to greatly speed up the discovery process. Increasingly, mining companies are using these systems to analyze data sets on geology, geochemistry, and geophysics all at once, in hopes of spotting correlations that would not be apparent to a human. 

By combining this approach with the AI decision-making tool developed with Stanford, KoBold is betting it can boost discovery rates by a factor of 20, says Josh Goldman, the company’s chief technology officer. KoBold says the approach will also reduce the environmental impact of exploration because it will mean fewer wasted drill holes in the ground. 

Unlike several other data science companies that focus on mining, KoBold isn’t selling a service. Instead, it’s developing software tools to guide its own exploratory work, which means KoBold gets to decide where prospecting will occur. And it claims it will work only in areas where it can do so ethically and with community buy-in.

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By: Maddie Stone
Title: The big tech quest to find the metals needed for the energy overhaul
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Published Date: Wed, 11 Aug 2021 09:00:00 +0000

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