Geochemical patterns of hydrothermal mineral deposits associated with calc-alkalic and alkali-calcic igneous rocks as evaluated with neural networks.
AuthorWilt, Jan Carol.
Committee ChairGuilbert, John M.
Poulton, Mary M.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractSix 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 <0.6 with ilmenite only in reduced subclasses. Lead-zinc-silver deposits as at Tombstone and Tintic are related to oxidized alkali-calcic igneous rocks. Polymetallic lead-zinc-copper-tin-silver deposits, such as Santa Eulalia and Tempiute, Nevada, are associated with weakly oxidized alkali-calcic rocks. Tin-silver deposits of Llallagua and Potosi are correlated with reduced alkali-calcic intrusives. Porphyry copper deposits as at Ray and Sierrita are connected with oxidized calc-alkalic plutons. Gold-rich porphyry copper deposits, such as Copper Canyon and Morenci are linked to weakly oxidized calc-alkalic plutons. Disseminated gold deposits, such as Chimney Creek, Nevada, are temporally and chemically correlated with reduced calc-alkalic igneous rocks, although physical connections between plutons and Carlin-type deposits remain unconfirmed. Magma series classification and neural networks have profound applications and implications to exploration, alteration and zoning studies, and metallogenesis.