Geologists have developed a machine learning technique that highlights the potential for further deposits of the critical metal tungsten in SW England.

Tungsten is an essential component of high-performance steels but global production is strongly influenced by China and western countries are keen to develop alternative sources.

The work, published in the leading journal Geoscience Frontiers, has been led by Dr Chris Yeomans, from 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 SW England.

These findings are then applied across the wider region to predict areas where tungsten mineralisation is more likely and might have previously been overlooked. The same methodology could be applied to help in the exploration for 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 really pleased with the methodology developed and the results of this study.

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