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Base Metals

Chart: How Every Commodity Performed in 2016

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Presented by:    Sierra Metals

2016 Commodity Performance

2016 Commodity Performance

It was an up and down year for commodities, but things ultimately finished in the black.

The S&P Goldman Sachs Commodity Index (GSCI) climbed 10.1% on the year – it was just enough to edge out the S&P 500, which ended 2016 with a 9.5% return.

Winners in 2016

The biggest winners on the year were base metals and the oil and gas sector.

Here’s how base metals did:

Base MetalQ1Q2Q3Q42016
Iron Ore37.0%-6.2%6.3%31.1%81.0%
Zinc20.0%13.1%-3.2%26.1%65.7%
Nickel-3.1%13.9%11.9%-5.0%17.3%
Aluminum3.8%7.2%1.4%4.0%17.3%
Copper0.1%3.9%-0.5%13.1%17.1%

Iron ore and zinc were the best performing commodities on the face of the planet in 2016. Iron finished up 81%, its first calendar gain in four years. Meanwhile, zinc shot up 65.7% on the year as major zinc mines shut down, and supply stockpiles dwindled.

Oil and gas also posted a major comeback in 2016:

EnergyQ1Q2Q3Q42016
Natural gas-17.0%53.3%-2.7%28.0%58.5%
Oil (Brent)0.6%35.1%-1.2%13.6%52.4%
Oil (WTI)-3.2%37.3%-2.1%11.4%44.9%

It was a volatile year overall, but it appears that the worst of the downturn in energy prices is over.

Losers in 2016

Not all energy-related commodities could be so lucky.

Uranium continued its epic nosedive, losing -41.6% on the year. U3O8 now trades for $20.25/lb, a tiny fraction of its previous highs of over $100/lb in 2007.

Energy LosersQ1Q2Q3Q42016
Uranium-16.0%-7.4%-12.0%-14.7%-41.6%
Coal0.5%-9.3%1.3%0.0%-7.7%

Coal has also performed abysmally, at least in North America where CAPP prices finished down on the year -7.7%. We previously showed the decline of coal in three charts, and it seems that coal will likely continue to be an unpopular choice for utility companies in the U.S. and Canada.

That said, it is worth mentioning that Australian coal prices went bonkers earlier this year due to a Chinese administrative oversight.

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Base Metals

20 Common Metal Alloys and What They’re Made Of

You can’t find stainless steel, brass, sterling silver, or white gold on the periodic table. Learn about 20 common metal alloys, and what they are made from.

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Every day, you’re likely to encounter metals that cannot be found anywhere on the periodic table.

You may play a brass instrument while wearing a white gold necklace – or maybe you cook with a cast iron skillet and store your leftovers in a stainless steel refrigerator.

It’s likely that you know these common metal alloys by name, and you can probably even imagine what they look and feel like. But do you know what base metals these alloys are made of, exactly?

Common Metal Alloys

Today’s infographic comes to us from Alan’s Factory Outlet, and it breaks down metal and non-metal components that go into popular metal alloys.

In total, 20 alloys are highlighted, and they range from household names (i.e. bronze, sterling silver) to lesser-known metals that are crucial for industrial purposes (i.e. solder, gunmetal, magnox).

20 Common Metal Alloys and What They

Humans make metal alloys for various reasons.

Some alloys have long-standing historical significance. For example, electrum is a naturally-occurring alloy of gold and silver (with trace amounts of copper) that was used to make the very first metal coins in ancient history.

However, most of the common metal alloys on the above list are actually human inventions that are used to achieve practical purposes. Some were innovated by brilliant metallurgists, while others were discovered by fluke, but they’ve all had an ongoing impact on our species over time.

Alloys with an Impact

The Bronze Age (3,000 BC – 1,200 BC) is an important historical period that is rightfully named after one game-changing development: the ability to use bronze. This alloy, made from copper and tin, was extremely useful to our ancestors because it is much stronger and harder than its component metals.

Steel is another great example of an alloy that has changed the world. It is one of the most important and widely-used metals today. Without steel, modern civilization (skyscrapers, bridges, etc.) simply wouldn’t be possible.

While nobody knows exactly who invented steel, the alloy has a widely-known cousin that was likely invented in somewhat accidental circumstances.

In 1912, English metallurgist Harry Brearley had been tasked with finding a more erosion-resistant steel for a small arms manufacturer, trying many variations of alloys with none seeming to be suitable. However, in his scrap metal heap – where almost all of the metals he tried were rusting – there was one gun barrel that remained astonishingly untouched.

The metal alloy – now known to the world as stainless steel – was a step forward in creating a corrosion-resistant steel that is now used in many applications ranging from medical uses to heavy industry.

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Base Metals

How AI and Big Data Will Unlock the Next Wave of Mineral Discoveries

Mineral exploration produces massive amounts of data. With AI, geologists can produce geological insights from this data to make the next discovery.

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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 SpendEstimated Value of DiscoveriesValue/Spend ratio
Australia$13 billion$13 billion0.97
Canada$25 billion$19 billion0.77
USA$10 billion$5 billion0.48
Latin America$33 billion$19 billion0.57
Pacific/SE Asia$8 billion$4 billion0.49
Africa$20 billion$23 billion1.19
Western Europe$4 billion$2 billion0.42
Rest of World$27 billion$8 billion0.32
Total$140 billion$93 billion0.57

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:

  1. Data collection
  2. Information retrieval
  3. Prediction
  4. Judgment
  5. 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.

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