Digging for Green Tech How KoBold Metals uses AI to find rare minerals

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2 min read
Chart showing the mine supply, recycling and supply gap of different minerals' production from 2025 to 2050

The metals needed to meet rocketing demand for electric cars and renewable power plants are in short supply. A startup is using machine learning to discover new sources.

What's new: KoBold Metals invested $150 million to develop a copper mine in Zambia. With funding backed by OpenAI founder Sam Altman, Jeff Bezos, Richard Branson, and Bill Gates, the four-year-old startup based in Berkeley, California, previously forged partnerships with mining giants BHP and Rio Tinto.

How it works: The Zambia site may yield enough copper to produce 100 million electric vehicles, Bloomberg reported. The readiest sources of copper, cobalt, nickel, lithium, and rare-earth elements — minerals crucial to development of next-generation energy sources — have already been developed. KoBold identifies locations that have been overlooked or rejected using conventional methods and where valuable ore may be buried deep underground.

  • To search for undiscovered deposits of a given ore, KoBold trains a model to identify possible deposits using a proprietary dataset that includes geological data culled from academic papers, satellite imagery, soil analyses, and handwritten field reports. The model outputs a map showing likely deposits.
  • Having identified a viable deposit, the company collects data from the site to train models that pinpoint the best place to drill. For instance, cables on the ground can gauge interactions between electromagnetic waves and subsurface minerals. Models trained on such data estimate mineral composition beneath particular areas.
  • Off-site geologists and data scientists develop geological hypotheses based on the on-site measurements. They calculate a drill hole that intersects with potential deposits using Bayesian inference and other techniques.

Behind the news: Oil and gas producers use a variety of AI techniques to find oil and gas deposits and other phases of production. In exploration, models typically learn from large quantities of seismic data to evaluate areas below the surface for qualities like porosity and saturation, helping to identify sweet spots. Neural networks are typically used to home in on the most promising targets. Other architectures have proven useful in locating wells, predicting well pressure, and related tasks.

Yes, but: Kobold’s approach is not yet proven. It uses data from some parts of the world to discover metal deposits in others, while minerals in the Earth’s crust can occur under widely varying conditions, Wired reported.

Why it matters: Heavy metals and rare earth minerals are crucial raw materials for components in batteries, electric motors, wind turbines, and portable electronics. But extracting these resources is costly and ecologically fraught; only one in 100 exploratory boreholes bears fruit. If machine learning can reduce the risk, it may make prospecting more economical and environmentally friendly.

We're thinking: It’s good to see the mining industry doesn’t take AI for granite.


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