Big Data Points to New Minerals, New Deposits
By Shaunna Morrison and Robert Hazen, Deep Carbon Observatory
The quest for new mineral deposits is incessant, but until recently mineral discovery has been more a matter of luck than scientific prediction. All that may change thanks to big data.
In a paper published Aug. 1 by American Mineralogist, we report the first application to mineralogy of network theory—best known for analysis of e.g. the spread of disease, terrorist networks, or Facebook connections.
The results pioneer a potential way to reveal mineral diversity and distribution worldwide, their evolution through deep time, new trends, and new deposits of valuable minerals such as gold or copper.
Humans have collected a vast amount of information on Earth’s more than 5,200 known mineral species (each of which has a unique combination of chemical composition and atomic structure).
Millions of mineral specimens from hundreds of thousands of localities around the world have been described and catalogued. Databases containing details of where each mineral was discovered, all of its known occurrences, and the ages of those deposits are large and growing by the week.
Databases also record essential information on chemical compositions and a host of physical properties, including hardness, colour, atomic structure, and more.
What is “big data”?
Coupled with data on the surrounding geography, the geological setting, and coexisting minerals, we now have access to “big data” resources ripe for analysis.
Until recently, we didn’t have the necessary modelling and visualization tools to capitalize on these giant stockpiles of information.
Big data is a big thing! You hear about it in all kinds of fields—medicine, commerce; even the US National Security Agency uses it to analyze phone records—but until recently, no one had applied big data methods to mineralogy and petrology.
Network analysis offers new insight into minerals, just as complex data sets offer important understanding of social media connections, city traffic patterns, and metabolic pathways, to name a few examples.
And we believe that this is going to expand the rate of mineral discovery in ways that can’t even be imagined.
The network analysis technique enables the representation—within a single graph—of data from multiple variables on thousands of minerals sampled from hundreds of thousands of locations.
These visualizations can reveal patterns of occurrence and distribution that might otherwise be hidden within a spreadsheet.
In other words, big data provides an intimate picture of which minerals coexist with each other, as well as what geological, physical, chemical, and (perhaps most surprising) biological characteristics are necessary for their appearance.
WHAT’S THE OUTCOME?
From those insights, it’s a relatively simple step to predict what minerals are missing from scientific lists, as well as where to go to find new deposits.
Network analysis can provide visual clues to mineralogists regarding where to go and what to look for. This is a new idea in the paper expected to open up an entirely new direction in mineralogy
Already the technique has been used to predict 145 missing carbon-bearing minerals and where to find them, leading to creation of the Deep Carbon Observatory’s Carbon Mineral Challenge (http://mineralchallenge.net/). Eleven have been found so far.
The estimate came from a statistical analysis of carbon-bearing minerals known today, then extrapolating how many scientists should be looking for.
We have used the same kinds of techniques to predict that at least 1,500 minerals of all kinds are “missing” to predict what some of them are, and where to find them.
In addition to enabling the prediction of minerals unknown to science today and the location of new deposits, this technique will shed light on how minerals have changed through geologic time which, coupled with our knowledge of biology, is leading to new insights regarding the co-evolution of the geosphere and biosphere.
In action
In a test case, we explored minerals containing copper, which play critical roles in modern society (e.g., pipes, wires), as well as essential roles in biological evolution. The element is extremely sensitive to oxygen, so the nature of copper in a mineral offers a clue to the level of oxygen in the atmosphere at the time the mineral formed.
The investigation also included an analysis of common minerals in igneous rocks—those formed from a hot molten state. The mineral networks of igneous rocks revealed through big data recreated “Bowen’s reaction series” (based on Norman L. Bowen’s painstaking lab experiments in the early 1900s), which shows how a sequence of characteristic minerals appears as the magma cools.
The analysis showed the exact same sequence of minerals embedded in the mineral networks.
Network analysis has numerous potential applications in geology, both for research and mineral exploration.
Dr. Morrison also hopes to use network analysis to reveal the geologic history of other planets. She and Dr. Hazen are members of the NASA Mars Curiosity Rover team identifying Martian minerals through X-ray diffraction data sent back to Earth. By applying these tools to analyze sedimentary environments on Earth, she believes scientists may also start answering similar questions about Mars.
Minerals provide the basis for all our material wealth, not just precious gold and brilliant gemstones, but in the brick and steel of every home and office, in cars and planes, in bottles and cans, and in every high-tech gadget from laptops to iPhones.
Minerals form the soils in which we grow our crops, they provide the gravel with which we pave our roads, and they filter the water we drink.
This new tool for understanding minerals represents an important advance in a scientific field of vital interest.
Led by Shaunna Morrison of the Deep Carbon Observatory and DCO Executive Director Robert Hazen (both at the Carnegie Institution for Science in Washington, D.C.), the paper’s 12 authors include DCO colleagues Peter Fox and Ahmed Eleish at the Keck Foundation sponsored Deep-Time Data Infrastructure Data Science Teams at Rensselaer Polytechnic Institute, Troy NY.
Citation: Morrison SM, Liu C, Eleish A, Prabhu A, Li C, Ralph J, Downs RT, Golden JJ, Fox P, Hummer DR, Meyer MB, Hazen RM (2017) Network analysis of mineralogical systems. American Mineralogist doi: https://doi.org/10.2138/am-2017-6104CCBYNCND
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