Machine Learning Research May Help Discover Tungsten Deposits in England

Geologists have developed a machine learning technique that highlights the potential for further tungsten deposits in southwest England. Tungsten is an essential component of high-performance steel, but global production is strongly influenced by China, and western countries are keen to develop alternative sources.

This work was published in the leading journal Geoscience Frontiers and was led by Dr. Chris Yeomans of the Camborne School of Mines, and involved geoscientists from the University of Nottingham, Geological Survey of Finland (GTK) and the British Geological Survey. The research applies machine learning to multiple existing datasets to examine the geological factors that have resulted in known tungsten deposits in southwest England.

Then, apply these findings to a wider area to predict that the possibility of tungsten mineralization is higher, which may have been overlooked before. The same methodology could be applied to help in the exploration of other metals around the world.

Dr. Yeomans, a Postdoctoral Research Fellow at the Camborne School of Mines, based at the University of Exeter's Penryn Campus in Cornwall said: "We're pleased with the methodology developed and the results of this study.

"Southwest of the UK is already the focus of UK mineral exploration for tungsten but we wanted to demonstrate that new machine learning approaches may provide additional insights and highlight areas that might otherwise be overlooked."

Southwest England hosts the fourth biggest tungsten deposit in the world (Hemerdon, near Plympton), which resulted in the UK being the sixth-biggest global tungsten producer in 2017; the mine is currently being re-developed by Tungsten West Limited.

The Redmoor tin-tungsten project developed by Cornwall Resources Ltd. has also been identified as a mineral deposit of potentially global significance. This new study shows that the potential of tungsten deposits may be greater, and has drawn praise from people currently involved in the development of tungsten resources in southwest England.

James McFarlane from Tungsten West said: "Tungsten has only been of economic interest in the last 100 years or so, during which exploration efforts for this critical metal have generally been short-lived. The overall integration of available data to develop tungsten ore potential models in areas with world-class potential is very encouraging."

Brett Grist of Cornwall Resources added: "Our work of machine learning has shown that applying modern techniques can reveal world-class deposits in this historic and globally-significant mining district. Dr. Yeomans' assertion, that the likelihood of discoveries of tungsten deposits may be enhanced by a high-resolution gravity survey, is something in which we see great potential. Indeed, such a program could stimulate the discovery of economically significant deposits of a suite of critical metals, here in the southwest of England, for years to come."

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