Mining
How AI and Big Data Will Unlock the Next Wave of Mineral Discoveries
How AI and Big Data Will Unlock the Next Mineral Discovery
Emerging technologies such as artificial intelligence (AI) and machine learning are rapidly proving their value across many industries.
Today’s infographic comes from GoldSpot Discoveries, and it shows that when this tech is applied to massive geological data sets, that there is growing potential to unlock the next wave of mineral discoveries.
Mineral Exploration: Fortunes Go to the Few
Discovering new sources of minerals, such as copper, gold, or even cobalt, can be notoriously difficult but also very rewarding. According to Goldspot, the chance of finding a new deposit is around 0.5%, with odds improving to 5% if exploration takes place near a known resource.
On the whole, mineral exploration has not been a winning prospect if you compare the total dollar spend and the actual value of the resulting discoveries.
Measuring Discovery Performance by Region (2005 to 2014)
Region | Exploration Spend | Estimated Value of Discoveries | Value/Spend ratio |
---|---|---|---|
Australia | $13 billion | $13 billion | 0.97 |
Canada | $25 billion | $19 billion | 0.77 |
USA | $10 billion | $5 billion | 0.48 |
Latin America | $33 billion | $19 billion | 0.57 |
Pacific/SE Asia | $8 billion | $4 billion | 0.49 |
Africa | $20 billion | $23 billion | 1.19 |
Western Europe | $4 billion | $2 billion | 0.42 |
Rest of World | $27 billion | $8 billion | 0.32 |
Total | $140 billion | $93 billion | 0.57 |
Figures in 2014 dollars. (Source: MinEx Consulting, March 2015)
Aside from the geographic insights, on the surface this data reveals that mineral exploration does not pay for itself. That said, there are still significant discoveries worth billions of dollars – it’s just the returns go inordinately to a few small players that make big finds.
Much of the money spent on exploration may not have produced the next great discovery, but you can be sure it created massive volumes of data that could be used for further refining of exploration models.
So, What is the Problem?
Every exploration failure or success produces geological insights. The mineral exploration process is the source of massive amounts of data in the form of soil samples, chip samples, geochemistry, drill results, and assay results. Each drill hole is a tiny snapshot into the processes that form the earth.
A single drill hole can create 200 megabytes of data and when there are many drill holes coupled with other types of information, an exploration project can produce terabytes of data. If you wanted to compare your one project to hundreds of others to find the best insights, the amount of data becomes dizzying.
All these data points are clues that can be used to find new mineral deposits, but to sort through them is too much for even an entire team of capable geologists.
Luckily, using today’s technology, this data can now be used to train computers to spot the areas showing similar patterns to past discoveries.
AI-Assistance
The true power of AI will be in its ability to empower technically trained professionals to make decisions in an increasingly complex and data-driven world.
Professor Ajay Agrawal, a noted academic in AI and founder of the University of Toronto’s Creative Destruction Lab, categorizes human activities into five categories:
- Data collection
- Information retrieval
- Prediction
- Judgment
- Action
He concludes that machines should do the first three and that humans – such as geologists, doctors, lawyers, investment bankers and others – should make the judgment calls and take the actions based on predictive capabilities of AI.
The mineral exploration industry presents a good example of how AI and big data can help technical professionals make discoveries faster, with less money, using a wide variety of data inputs created.
Opportunity Generator and the AI-friendly Future
AI can take the large amounts of data from many different projects in order to spot the right opportunities to further explore, building on decades of geological data from projects around the world.
The right technology can help reduce the risk inherent in exploration and lead to more mineral discoveries on budget, rewarding those that deployed their data most effectively. Companies that are able to harness this power will tip the scales in their favor.
As a result, mineral exploration is no longer so much an art of interpretation – but instead, it becomes closer to a pure science, giving geologists a whole-field perspective of all the data.
Uranium
Charted: Global Uranium Reserves, by Country
We visualize the distribution of the world’s uranium reserves by country, with 3 countries accounting for more than half of total reserves.
Charted: Global Uranium Reserves, by Country
This was originally posted on our Voronoi app. Download the app for free on iOS or Android and discover incredible data-driven charts from a variety of trusted sources.
There can be a tendency to believe that uranium deposits are scarce from the critical role it plays in generating nuclear energy, along with all the costs and consequences related to the field.
But uranium is actually fairly plentiful: it’s more abundant than gold and silver, for example, and about as present as tin in the Earth’s crust.
We visualize the distribution of the world’s uranium resources by country, as of 2021. Figures come from the World Nuclear Association, last updated on August 2023.
Ranked: Uranium Reserves By Country (2021)
Australia, Kazakhstan, and Canada have the largest shares of available uranium resources—accounting for more than 50% of total global reserves.
But within these three, Australia is the clear standout, with more than 1.7 million tonnes of uranium discovered (28% of the world’s reserves) currently. Its Olympic Dam mine, located about 600 kilometers north of Adelaide, is the the largest single deposit of uranium in the world—and also, interestingly, the fourth largest copper deposit.
Despite this, Australia is only the fourth biggest uranium producer currently, and ranks fifth for all-time uranium production.
Country | Share of Global Reserves | Uranium Reserves (Tonnes) |
---|---|---|
🇦🇺 Australia | 28% | 1.7M |
🇰🇿 Kazakhstan | 13% | 815K |
🇨🇦 Canada | 10% | 589K |
🇷🇺 Russia | 8% | 481K |
🇳🇦 Namibia | 8% | 470K |
🇿🇦 South Africa | 5% | 321K |
🇧🇷 Brazil | 5% | 311K |
🇳🇪 Niger | 5% | 277K |
🇨🇳 China | 4% | 224K |
🇲🇳 Mongolia | 2% | 145K |
🇺🇿 Uzbekistan | 2% | 131K |
🇺🇦 Ukraine | 2% | 107K |
🌍 Rest of World | 9% | 524K |
Total | 100% | 6M |
Figures are rounded.
Outside the top three, Russia and Namibia both have roughly the same amount of uranium reserves: about 8% each, which works out to roughly 470,000 tonnes.
South Africa, Brazil, and Niger all have 5% each of the world’s total deposits as well.
China completes the top 10, with a 3% share of uranium reserves, or about 224,000 tonnes.
A caveat to this is that current data is based on known uranium reserves that are capable of being mined economically. The total amount of the world’s uranium is not known exactly—and new deposits can be found all the time. In fact the world’s known uranium reserves increased by about 25% in the last decade alone, thanks to better technology that improves exploration efforts.
Meanwhile, not all uranium deposits are equal. For example, in the aforementioned Olympic Dam, uranium is recovered as a byproduct of copper mining occurring at the same site. In South Africa, it emerges as a byproduct during treatment of ores in the gold mining process. Orebodies with high concentrations of two substances can increase margins, as costs can be shared for two different products.
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