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1-8 Wilt, Jan C., 1993, Geochemical patterns of hydrothermal mineral deposits associated with calc-alkalic and alkali-calcic igneous rocks as evaluated with neural networks: Tucson, AZ, University of Arizona, Ph. D. dissertation, 721 p.
1-8 Wilt, Jan C., 1993, Geochemical patterns of hydrothermal mineral deposits associated with calc-alkalic and alkali-calcic igneous rocks as evaluated with neural networks: Tucson, AZ, University of Arizona, Ph. D. dissertation, 721 p.
ABS
Six alkalinity and oxidation classes of fresh igneous rocks were correlatedwith trace elements in rock chip samples from temporally and spatially associatedore deposits. Learning vector quantization and back-propagation artificial neuralnetworks correctly classified 100 percent of whole rock oxides and 99 percent ofmineralized samples; discriminant analysis correctly classified 96 and 83 percent,respectively. The high degree of correlation between chemistries of igneousrocks and related mineralization implies genetic links between magmaticprocesses or sources and the ore deposits studied.
The petrochemical classification was evaluated by assigning 43 deposits toclasses defined on eight variation diagrams, training neural networks to classifyanalyses of 569 igneous and 887 mineralized samples, and testing the networks ontheir ability to classify new data. Whole rock analyses were obtained frommining districts in which trace element geochemistry was also available. Half thedata was eliminated using five alteration filter graphs. The K20 and Fe2O:/FeOversus SiO2 diagrams and iron mineralogy best defined alkalinity and oxidationclasses. Neural networks trained with 90, 80, 70, or 50 percent of the samplescorrectly classified 81 to 100 percent of randomly withheld data.
SiO2/K2O ratios of alkali-calcic igneous rocks are 14-20 and of calc-alkalic20-30. Fe2O3/FeO ratios are >0.8 with abundant magnetite and sphene foroxidized, 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 atTombstone and Tintic are related to oxidized alkali-calcic igneous rocks.Polymetallic lead-zinc-copper-tin-silver deposits, such as Santa Eulalia andTempiute, Nevada, are associated with weakly oxidized alkali-calcic rocks. Tin-silverdeposits of Llallagua and Potosi are correlated with reduced alkali-calcicintrusives. Porphyry copper deposits as at Ray and Sierrita are connected withoxidized calc-alkalic plutons. Gold-rich porphyry copper deposits, such as CopperCanyon and Morenci are linked to weakly oxidized calc-alkalic plutons.Disseminated gold deposits, such as Chimney Creek, Nevada, are temporally andchemically correlated with reduced calc-alkalic igneous rocks, although physicalconnections between plutons and Carlin-type deposits remain unconfirmed.
Magma series classification and neural networks have profoundapplications and implications to exploration, alteration and zoning studies, andmetallogenesis.
Key words
1-8 Wilt, Jan C., 1993, Geochemical patterns of hydrothermal mineral deposits associated with calc-alkalic and alkali-calcic igneous rocks as evaluated with neural networks: Tucson, AZ, University of Arizona, Ph. D. dissertation, 721 p.
ABS
Six alkalinity and oxidation classes of fresh igneous rocks were correlatedwith trace elements in rock chip samples from temporally and spatially associatedore deposits. Learning vector quantization and back-propagation artificial neuralnetworks correctly classified 100 percent of whole rock oxides and 99 percent ofmineralized samples; discriminant analysis correctly classified 96 and 83 percent,respectively. The high degree of correlation between chemistries of igneousrocks and related mineralization implies genetic links between magmaticprocesses or sources and the ore deposits studied.
The petrochemical classification was evaluated by assigning 43 deposits toclasses defined on eight variation diagrams, training neural networks to classifyanalyses of 569 igneous and 887 mineralized samples, and testing the networks ontheir ability to classify new data. Whole rock analyses were obtained frommining districts in which trace element geochemistry was also available. Half thedata was eliminated using five alteration filter graphs. The K20 and Fe2O:/FeOversus SiO2 diagrams and iron mineralogy best defined alkalinity and oxidationclasses. Neural networks trained with 90, 80, 70, or 50 percent of the samplescorrectly classified 81 to 100 percent of randomly withheld data.
SiO2/K2O ratios of alkali-calcic igneous rocks are 14-20 and of calc-alkalic20-30. Fe2O3/FeO ratios are >0.8 with abundant magnetite and sphene foroxidized, 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 atTombstone and Tintic are related to oxidized alkali-calcic igneous rocks.Polymetallic lead-zinc-copper-tin-silver deposits, such as Santa Eulalia andTempiute, Nevada, are associated with weakly oxidized alkali-calcic rocks. Tin-silverdeposits of Llallagua and Potosi are correlated with reduced alkali-calcicintrusives. Porphyry copper deposits as at Ray and Sierrita are connected withoxidized calc-alkalic plutons. Gold-rich porphyry copper deposits, such as CopperCanyon and Morenci are linked to weakly oxidized calc-alkalic plutons.Disseminated gold deposits, such as Chimney Creek, Nevada, are temporally andchemically correlated with reduced calc-alkalic igneous rocks, although physicalconnections between plutons and Carlin-type deposits remain unconfirmed.
Magma series classification and neural networks have profoundapplications and implications to exploration, alteration and zoning studies, andmetallogenesis.
Key words
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