Geochemical patterns of hydrothermal mineral deposits associated with calc-alkalic and alkali-calcic igneous rocks as evaluated with neural networks.

Six alkalinity and oxidation classes of fresh igneous rocks were correlated with trace elements in rock chip samples from temporally and spatially associated ore deposits. Learning vector quantization and back-propagation artificial neural networks correctly classified 100 percent of whole rock oxides and 99 percent of mineralized samples; discriminant analysis correctly classified 96 and 83 percent, respectively. The high degree of correlation between chemistries of igneous rocks and related mineralization implies genetic links between magmatic processes or sources and the ore deposits studied. The petrochemical classification was evaluated by assigning 43 deposits to classes defined on eight variation diagrams, training neural networks to classify analyses of 569 igneous and 887 mineralized samples, and testing the networks on their ability to classify new data. Whole rock analyses were obtained from mining districts in which trace element geochemistry was also available. Half the data was eliminated using five alteration filter graphs. The K₂O and Fe₂O₃/FeO versus SiO₂ diagrams and iron mineralogy best defined alkalinity and oxidation classes. Neural networks trained with 90, 80, 70, or 50 percent of the samples correctly classified 81 to 100 percent of randomly withheld data. SiO₂/K₂O ratios of alkali-calcic igneous rocks are 14-20 and of calc-alkalic 20-30. Fe₂O₃/FeO ratios are >0.8 with abundant magnetite and sphene for oxidized, 0.5-1.2 with magnetite, sphene, and rare ilmenite for weakly oxidized, and