Using big data analysis in mineralogy provides a way to not only “predict” minerals missing from those known to science, but where to find them and where to find new deposits of economically important minerals and fossil fuels.
In a paper published in American Mineralogist, scientists report on the first application of network theory used in mineralogy. You might be familiar with network theory through its use in the analysis of the spread of disease or even Facebook connections.
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.
Using the unique method, 10 new carbon-bearing minerals have been discovered, meaning it could be widely used in exploration, says the study, revealing mineral diversity and distribution worldwide, mineral evolution through deep time, new trends, and new deposits.
“The quest for new mineral deposits is incessant, but until recently mineral discovery has been more a matter of luck than scientific prediction,” says Dr. Morrison, according to Phys.Org. “All that may change thanks to big data.”
What can big data tell us about mineral deposits?
In this instance, we’re looking at several sciences – Geology, mineralogy, Earth science and others. Massive amounts of information and other types of data have been collected that cover how, where and when mineral and fossil fuel deposits have formed, such as by the cooling of lava after volcanic eruptions, or by movement of tectonic plates during earthquakes millennia ago.
The point is, humans have collected a huge amount of information on the over 5,200 known minerals, each one having a unique combination of chemical composition and atomic structure. All this information has been described and cataloged, including the hundreds of thousands of locations around the world, the number of known occurrences and the ages of those deposits.
When we couple the mineral database with data on the geography, co-existing mineral deposits, and geological formations of the locale where the minerals were found, Earth scientists end up having access to “big data” resources requiring some “data mining.”
“Minerals occur on Earth in clusters,” said Robert Hazen, executive director of the Deep Carbon Observatory at the Carnegie Institution for Science in Washington and an author of the study, reports Reuters. “When you see minerals together it’s very like the way that humans interact in social networks such as Facebook,” he said.
In talking about the network theory, Hazen cited Netflix and Amazon. For both companies, they can make recommendations on what books or movies a consumer might be interested in, based on the person’s past reading or viewing habits. “They are using vast amounts of data and make correlations that you could never make.”
Of the 10 rare minerals found using the new technique, two of them are called abellaite and parisaite. The mineral’s existence was predicted before they were ever found. However, they have no known economic value.
Gilpin Robinson, with the U.S. Geological Survey (USGS), was not involved in the study, but in an email to Reuters said: “The use of large data sets and analytical tools is very important in our studies of mineral and energy resources.”
