Very well preserved fossil vent chimneys from the Silurian Yaman-Kasy volcanogenic-hosted massive sulfide deposit in the Southern Urals range in decreasing temperatures from chalcopyrite-pyrite black smoker to sphalerite-chalcopyrite-marcasite-pyrite gray smoker to sphalerite-quartz-barite white smoker assemblages. Laserablation ICPMS analyses show systematic trace element distribution patterns across chimneys. Coarse-grained layers of chalcopyrite in the central conduits are relatively high in Se and Sn but are low in other elements. Chalcopyrite at the margins of such layers is enriched in Bi, Co, Au, Ag, Pb, Mo, Te, and As, which reside in microinclusions of tellurides and/or sulfoarsenides. Sphalerite in the conduits and the outer chimney wall contains elevated Sb, As, Pb, Co, Mn, U, and V. Antimony, As, and Pb reside in microinclusions of a galena-fahlore assemblage, whereas the Co and Mn likely substitute for Zn 2+ in the sphalerite structure. The highest concentrations of most trace elements are found in colloform pyrite within the outer wall of the chimneys and likely result from rapid precipitation under high-temperature-gradient conditions. The trace element concentrations in the outer-wall colloform pyrite decrease in the following order, from the outer wall inward: Tl > Ag > Ni > Mn > Co > As > Mo > Pb > Ba > V > Te > Sb > U > Au > Se > Sn > Bi, governed by the strong temperature gradient. In contrast, pyrite in the high-to mid-temperature central conduits exhibit concentration of Se, Sn, Bi, Te, and Au. The zone between the inner conduit and outer wall is characterized by recrystallization of colloform pyrite to euhedral pyrite, which becomes depleted in all trace elements except Co, As and Se.The mineralogical and trace element variations between chimneys are likely due to increasing fO 2 and decreasing temperature caused by mixing of hydrothermal fluids with cold oxygenated seawater. Average values of Se (a high-temperature element) decrease in the order from black to gray to white smoker chimneys. The medium-temperature association (Te, Bi, Co, Mo, and Au) is typically present in the gray smoker chimneys. The white smoker chimneys are depleted in most elements except for Ag, Tl, Te, Sb, and As, probably due to the dilution of the vent fluid by seawater which penetrates deeper parts of the hydrothermal system. U and V are concentrated in the outer wall of most chimneys due to their extraction from seawater associated with the more reduced fluids of black and gray smokers. the present paper is to document trace element variations in the sulfide phases (pyrite, chalcopyrite, and sphalerite) from the three sulfide chimney types found in the deposit and, in particular, to focus on the possible causes of trace element zonation from rim to core in the chimneys. Regional Geologic SettingSeveral reviews have described the geologic setting and composition of the VHMS deposits in the Urals (Filatov and Shiray, 1988;Koroteev et al.

We report sulfur isotopic compositions of sulfi des of various paragenetic stages in the giant Sukhoi Log sediment-hosted orogenic Au deposit in Russia. The overall mean value and the signifi cant variability in early pyrite indicate that the sulfur was from the reduction of seawater sulfate. The later generations of sulfi de have δ 34 S values in successively smaller ranges, coincident with the mode that is around the median value of the whole data set. Together with textural evidence, sulfi de trace element data, and gold occurrence, the data demonstrate that metamorphism has gradually homogenized the early sulfur, accompanied by the segregation of quartz and the release of Au from the lattice of early pyrite and its reprecipitation as inclusions in later pyrite. The S isotopic compositions of sulfi des in Sukhoi Log, and many other major orogenic Au deposits hosted in sedimentary rocks of various ages, show a pattern generally parallel to the seawater sulfate curve through geologic time, indicating that the sulfur in most sediment-hosted orogenic Au deposits was probably also originally from the reduction of seawater sulfate. We conclude that sulfi dation and gold mineralization in many sediment-hosted orogenic gold deposits was early during basin evolution when seawater was the principal active fl uid, rather than later, during or after basin inversion, as proposed in current models.

The basalt-hosted Semenov-2 hydrothermal field on the Mid-Atlantic Ridge is host to a rather unique Cu-Zn–\ud rich massive sulfide deposit, which is characterized by high Au (up to 188 ppm, average 61 ppm, median\ud 45 ppm) and Ag (up to 1,878 ppm, average 490 ppm, median 250 ppm) contents. The largest proportion of\ud visible gold is associated with abundant opal-A, which precipitated after a first generation of Cu, Fe, and Zn\ud sulfides and before a second generation of Fe and Cu sulfides. Only rare native gold grains were found in earlier\ud sulfides. Fluid inclusions in opal-A associated with native gold indicate precipitation at 300° ± 40°C from\ud fluids of salinity higher than that of seawater (3.5–6.8 wt % NaCl equiv). According to laser ablation-inductively\ud coupled plasma-mass spectrometry analyses, invisible gold is concentrated in secondary covellite (23–227 ppm)\ud rather than in the primary sulfides (<1 ppm). Silver minerals (native silver, stutzite, and naumannite) rarely\ud occur in the sulfides and in aragonite associated with opal-A; invisible silver was detected in all sulfides, but,\ud again, covellite contains more Ag (>1,000 ppm) than all other sulfides (<250 ppm). Covellite replacing Zn\ud sulfides (covellite-A) is enriched in all analyzed trace elements relative to covellite replacing Cu-Fe sulfides\ud (covellite-B). The enrichment of covellite-A in trace elements may be related to the dissolution of inclusions\ud of various minerals hosted in former sphalerite, which were the source for Au and Ag (native gold), Pb and Tl\ud (galena), Se (chalcopyrite, Se-bearing galena, naumannite), Te and Bi (Bi tellurides), As (tennantite, chalcopyrite),\ud and Sb (tennantite). The formation of covellite-A was favored by hydrothermal fluid/seawater mixing or\ud direct oxidation of sulfides by seawater, as suggested by the relatively high contents of typical “seawater” elements\ud (U and V). The degree of seawater involvement was apparently lower for covellite-B.\ud Although the Semenov-2 field is basalt hosted, several geochemical features of the massive sulfides studied\ud are similar to those of the Mid-Atlantic Ridge ultramafic-hosted Cu-Zn–rich massive sulfides, such as Fe:Cu:Zn\ud ratios close to 1:1:1, high Sn, Se, Au ,and Ag contents, and high Au/Ag ratios. However, the strong enrichment\ud in SiO2, the moderate Mn and Co contents, very low Ni contents, and the Co/Ni ratio >1 are more consistent\ud with a mafic signature. Thermodynamic modeling of hydrothermal fluids produced by reactions between various\ud proportions of seawater and basalt or peridotite at 350°C shows that mineral assemblages broadly similar to\ud those of the Semenov-2 deposit can precipitate from fluids produced in a mafic environment, but that Au and\ud Ag minerals are not predicted to precipitate from such fluids over a wide temperature range. These results suggest\ud that an additional contribution to the hydrothermal system is required in order to achieve saturation in precious\ud metals. A magmatic input is suggested ...

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Faced with ongoing depletion of near-surface ore deposits, geologists are increasingly required to explore for deep deposits or those lying beneath surface cover. The result is increased drilling costs and a need to maximize the value of the drill hole samples collected. Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) analysis of pyrite is one tool that is showing promise in deep exploration. Since the trace element content of pyrite approximates the composition of the fluid from which it precipitated and the crystallization mechanism, the trace element characteristics can be used to predict the type of deposit with which a pyritic sample is associated. This possibility, however, is complicated by overlapping trace element abundances for many deposit types. The solution lies with simultaneous comparison of multiple trace elements through rigorous statistical analysis. Specifically, we used LA-ICP-MS pyrite trace element data and Random Forests, an ensemble machine learning supervised classifier, to distinguish barren sedimentary pyrite and five ore deposit categories: iron oxide copper-gold (IOCG), orogenic Au, porphyry Cu, sedimentary exhalative (SEDEX), and volcanic-hosted massive sulfide (VHMS) deposits. The preferred classifier utilizes in situ Co, Ni, Cu, Zn, As, Mo, Ag, Sb, Te, Tl, and Pb measurements to train the Random Forests. Testing of the Random Forests classifier using additional data from the same deposits and sedimentary basins (test data set) yielded an overall accuracy of 91.4% (94.9% for IOCG, 78.8% for orogenic Au, 81.1% for porphyry Cu, 93.6% for SEDEX, 97.2% for sedimentary pyrite, 91.8% for VHMS). Similarly, testing of the Random Forests classifier using data from deposits and sedimentary basins that did not have analyses in the training data set yielded an overall accuracy of 88.0% (81.4% for orogenic Au, 95.5% for SEDEX, 90.0% for sedimentary pyrite, 73.9% for VHMS; insufficient data was available to perform a blind test on porphyry Cu and IOCG). The performance of the classifier was further improved by instituting criteria (at least 40% of total votes from the Random Forests needed for a conclusive identification) to remove uncertain or inconclusive classifications, increasing the classifier's accuracy to 94.5% for the test data (94.6% for IOCG, 85.8% for orogenic Au, 87.8% for porphyry Cu, 95.4% for SEDEX, 98.5% for sedimentary pyrite, 94.6% for VHMS) and 93.9% for the blind test data (85.5% for orogenic Au, 96.9% for SEDEX, 96.7% for sedimentary pyrite, 84.6% for VHMS). The Random Forests classification models for pyrite trace element data can be used as a predictive modeling tool in greenfield terrains by providing an accurate indication of ore deposit type. This advance will assist mineral explorers by allowing early implementation of predictive ore deposit models when prospecting for ore deposits. Furthermore, the ability of the classifier to accurately identify pyrite of sedimentary origin will allow researchers interested in paleoenvironmental conditions of ...

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