WisdomTree’s Gannatti writes that AI might help hunt for metals for EV boom

Chris Gannatti, WisdomTree

Christopher Gannatti, Global Head of Research, WisdomTree has written a note on how Artificial Intelligence could help find metals needed for the electric-vehicle boom.

Gannatti writes that a typical electric car requires six times the mineral inputs of a conventional car, and a typical onshore wind plant requires nine times more mineral resources than a gas-fired power plant. 

Since 2010, the average amount of minerals needed for a new unit of power generation capacity has increased by 50 per cent as the share of renewables has risen, Gannatti says, noting that it’s possible that by 2040, lithium will see its demand growing by about 40 times, whereas graphic, cobalt and nickel could see demand grow by 20-25 times. 
 
“There is a need for both more natural resources and efficient means by which to use them more effectively,” Gannatti writes.
 
“WisdomTree has an established history of focusing on commodity markets and different thematic trends. Some of the most interesting concepts spring to light when these different types of initiatives intersect. It’s clear that:
 
1.    The world is more focused on the reduction of carbon emissions than at any point in recent history, and it could lead to certain actions, like a massive growth in electric vehicle demand. 
2.    A massive growth in electric vehicle demand leads to a massive growth in the demand for batteries, which require lots of raw materials. 
3.    If we as a global society are going to find more of these raw materials, new techniques will be required, similar to how horizontal drilling in shale was required to find more fossil fuel resources. What is that similarly new technique that can be used to find more cobalt, nickel or lithium, for example?
 
“Exploration geologists have a limited capacity to digest rich data provided by the new generation of exploration tools including geophysics, hyperspectral imaging, geochemistry and minerology. Properly calibrated machine learning techniques can be used to detect the patterns that indicate the best chances for the presence of certain types of ore—like lithium, copper, nickel or whatever might be desired. If one can envision a given site as a series of blocks of earth, and the computer model can track the data within each block of earth, there could be greater efficiencies.”
 
Gannatti believes that these techniques presently require significant supervision, because algorithms may see certain patterns in lakes, golf courses or waste water treatment plants as great potential exploration cites when processing satellite imagery. He writes that there has already been a useful tool developed in Zambia to deal with mafic rock, which, to the inexperienced could look like signals for deposits of copper. The benefit of properly trained algorithms can lead to recognition of these false signals. 
 
Gannatti references KoBold Metals. “KoBold Metals is a startup backed by Bill Gates’s Breakthrough Energy Ventures. The company aims to use artificial intelligence to find metals needed for the electric-vehicle boom. In a recent financing round, KoBold was able to raise USD192.5 million,” he writes.
One data collection technique involves a 115-foot-wide copper coil dangled from a helicopter, sending electromagnetic waves into the earth and having currents penetrate deep into the earth. 

Minerals have different electromagnetic properties, and the signals that come back contain valuable information of the types of minerals in different places. The possibility of nickel or cobalt deposits will have certain characteristics associated with what would be seen from the detector. This approach could cover more than 100 miles on a good day. It is unlikely that any single technique will hold all the answers, but the capability to 1) collect more data more efficiently and 2) overlay different types of data to better understand given regional deposits could be helpful, he says.
 
“Miners have indicated that new deposits will have to be found much deeper in the earth’s crust if the world is going to be able to meet its green energy needs of the future. It’s possible that certain patterns could be located in the upper layers of crust that could signal the potential presence of certain ores within the lower layers.
 
“It can take more than 10 years for new mines to become operational after companies receive different rights and permits. Discovering the best possible place to explore can also take a significant period of time. Most of the easily identifiable high-grade deposits have already been found, and investment in exploration has been declining. A rough rule of thumb is that for every 100 sites evaluated, one will turn up a profitable deposit. It’s possible that in recent years the actual figure is closer to one in one thousand. 
 
“KoBold believes that discovery rates can be boosted by a factor of about 20, and there is also a benefit if there are less unprofitable holes that need to be dug,” Gannatti says.
 
“A lot of initial work is being done in Canada. Canada has large amounts of survey data in the public domain, such as:
 
•    Narrative field reports
•    Timeworn geologic maps
•    Geochemical data on drill hole samples
•    Airborne magnetic and electromagnetic survey data
•    Lidar readings
•    Satellite imagery spanning many decades of exploration
 
“Once the information is compiled, KoBold explores the data using machine learning, for instance building a model that could predict which parts of ore deposits could have the highest concentrations of cobalt. It’s also possible that data can be added to models as its collected, allowing for adaptive changes to exploration strategy in real time. 
 
“KoBold partners with Stanford’s Center for Earth Resources Forecasting, which adds an additional layer of analytics to the mix in the form of an Artificial Intelligence (AI) ‘decision agent’ that can map an entire exploration plan. In a sense, this can quantify the uncertainty in KoBold’s model results and design a data collection plan that can lead to the reduction of this uncertainty. If the goal is set in this way, it could guide scientists and researchers to gather the types of data and information that could lead to the biggest impact on the result—the finding of ore deposits—and guide away from activities that could be less impactful.”
 
In conclusion, Gannatti writes that Artificial Intelligence (AI), at its core, is using the data available to give users the chance at more accurate predictions. 

“There is no guarantee that AI will immediately revolutionise mining or suddenly find and catalogue all minerals in the earth’s crust. However, in other fields there is no guarantee that AI will immediately find effective new drugs or be able to autonomously guide vehicles. In the near term, these are new approaches where researchers and practitioners will learn a lot that could lead to possibly breakthroughs from AI techniques being used in tandem with human experts.”

 

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