成矿作用
地质学
地球化学
矿床成因
古生物学
流体包裹体
石英
闪锌矿
黄铁矿
作者
Néstor Cano,Antoni Camprubí,Joaquín A. Proenza,Eduardo Gonzáléz-Partida
标识
DOI:10.1016/j.oregeorev.2024.105979
摘要
The final magmatic arc—Miocene in age—of the long-lived Sierra Madre del Sur igneous province in southern Mexico hosts several epithermal deposits that have hitherto received little attention. The Natividad Au-Ag(-Ge) epithermal deposit is one of them and holds a mining record of > 200 years. Here, we present the first petrogenetic and metallogenic study on the Natividad deposit, in which we use a multi-methodological approach to assess the genesis and evolution of the deposit and its associated igneous rocks. Natividad is mostly hosted by Oligocene–Miocene arc-related dacites (U-Pb zircon dates of 22.8–24.7 Ma), whose geochemical features suggest lower-crustal differentiation (i.e., involvement of garnet) at the base of a thickened continental crust. The mineralization occurs in three multi-stage quartz + carbonate veins and consists of Fe-poor sphalerite (<5 mol. % FeS), galena, chalcopyrite, acanthite, and marcasite (sulfide-dominated stage) overprinted by pearceite-polybasite, pyrargyrite-proustite, tetrahedrite-group minerals, electrum, and argyrodite (Ag8GeS6; Ag-sulfosalt-dominated stage). Geothermometry and fluid inclusion data for ore-bearing assemblages reveal fluid temperatures of 170°–400 °C and salinities of 14.6–19.5 wt% NaCl equiv. δ18O from 0.8 ‰ to 7.5 ‰ in mineralizing fluids suggests mixtures of magmatic brines and meteoric waters, while δ13C from –8.4 ‰ to 2.2 ‰ indicates the recycling of organic carbon from the meta-sedimentary basement. Further, δ34S from –3.2 ‰ to –0.3 ‰ in sulfides attest to magmatic S. These results align with an intermediate-sulfidation epithermal model, whereby upwelling magmatic ore-bearing brines precipitated the ores due to conductive cooling, dilution, and, locally, boiling. Our study highlights the metallogenic potential of Miocene magmatism in the eastern Sierra Madre de Sur.
科研通智能强力驱动
Strongly Powered by AbleSci AI