Researchers at Carnegie Science/Deep Carbon Observatory in USA detected recently a way to predict minerals missing from those known to science, where to find them, and where to look for new deposits of valuable minerals such as gold and copper.
Applying big data analysis to mineralogy offers a way to predict minerals missing from those known to science, where to find them, and where to find new deposits of valuable minerals such as gold and copper, according to a groundbreaking study.
In a paper published by American Mineralogist, scientists 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, they say, pioneer a way to reveal 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. "All that may change thanks to big data."
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, color, atomic structure, and more.
Coupled with data on the surrounding geography, the geological setting, and coexisting minerals, Earth scientists now have access to "big data" resources ripe for analysis.
Until recently, scientists didn't have the necessary modelling and visualization tools to capitalize on these giant stockpiles of information.
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.
"Big data is a big thing," says Dr. Hazen. "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."
"I think this is going to expand the rate of mineral discovery in ways that we can't even imagine now."
The network analysis technique enables Earth scientists to represent data from multiple variables on thousands of minerals sampled from hundreds of thousands of locations within a single graph.
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.
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.
Says Dr. Hazen: "Network analysis can provide visual clues to mineralogists regarding where to go and what to look for. This is a brand new idea in the paper and I think it will 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. Ten 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.
Source: Carnegie Science/Deep Carbon Observatory
