Two novel machine learning techniques are able to identify new, deeply buried porphyry copper deposits by characterizing magma fertility.
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To achieve such a goal, the researchers developed two algorithms, which they called ‘random forest’ and ‘deep neural network.’ They formulated the models using a global dataset of zircon chemistry, which is normally employed to evaluate the porphyry copper deposits in magma. Both models resulted in a classification accuracy of 90% or greater. The ‘random forest’ model exhibited a false-positive rate of 10%, whereas the ‘deep neural network’ model had a 15% false-positive rate. In comparison, traditional metrics report false positives at a 23%–66% rate.
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