Orogenic gold: Common or evolving fluid and metal sources through time

Goldfarb, Richard J.; Groves, David

doi:10.1016/j.lithos.2015.07.011

Cited by 781 publications

(337 citation statements)

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“…The ore-forming fluids at Liyuan have 13 C CO 2 values of −8.9 to −7.1‰, with an average of −7.9‰, which are similar to or slightly lower than those of average mantle value (-7 to -5‰, [92,93]), indicating that the carbon in the Liyuan deposit was likely derived from a deep magmatic source. In addition, the 13 C CO 2 values are slightly lower than those of average mantle and might be affected by an isotopic fractionation due to chemical reactions such as carbonate decomposition or contaminations of minor CH 4 gas related to metamorphism [94].…”

Section: Source Of Ore-forming Fluidsmentioning

confidence: 96%

Origin and Evolution of the Ore-Forming Fluids in the Liyuan Gold Deposit, Central North China Craton: Constraints from Fluid Inclusions and H-O-C Isotopic Compositions

Xiong

et al. 2017

Geofluids

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The Liyuan gold deposit is hosted within Archean basement metamorphic rocks and controlled by the NNE-trending faults in the central North China Craton. The ore-forming processes can be divided into three stages (early, middle, and late). Three types of primary fluid inclusions (FIs) are identified in the Liyuan, including pure carbonic, carbonic-aqueous, and aqueous inclusions. The primary FIs of three stages are mainly homogenized at temperatures of 318-408 ∘ C, 201-329 ∘ C, and 136-229 ∘ C, with salinities of 2.1-8.9, 0.5-12.4, and 0.4-6.3 wt.% NaCl equivalent, respectively. The main Au mineralization is related to the middle stage, and water-rock interaction caused rapid precipitation of gold in this stage. The initial ore-forming fluids were likely magmatic water or metamorphic fluid and mixed with meteoric water at later stages. Due to the lack of granite body at the present mining levels, we speculate that it was magmatic water that might have been exsolved from a concealed granite body at greater depth or it was metamorphic fluid that was directly transported from depth via deep faults. Based on all the available geological and geochemical evidence, we suggest that the Liyuan deposit belongs to orogenic gold deposit that located in the interior North China Craton.

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Section: Source Of Ore-forming Fluidsmentioning

confidence: 96%

Origin and Evolution of the Ore-Forming Fluids in the Liyuan Gold Deposit, Central North China Craton: Constraints from Fluid Inclusions and H-O-C Isotopic Compositions

Xiong

et al. 2017

Geofluids

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“…Geological settings of orogenic gold deposits are extremely complex, which resulted in highly variable host rocks (Goldfarb et al, 2001;Goldfarb and Groves, 2015). Gold mineralization is dominantly hosted by metagreywackes, metavolcanic, metasedimentary and metamorphic rocks of a variety of geological ages (Bark and Weihed, 2012;Groves et al, 2000;Fu et al, 2012).…”

Section: General Characteristics Of Orogenic Gold Depositsmentioning

confidence: 99%

“…They evolved along the southern Gondwana margin and the northern side of the Paleo-Tethys Ocean during the Paleozoic, and within the circum-Pacific accreted terranes in the Mesozoic-Tertiary (Goldfarb and Groves, 2015).…”

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confidence: 99%

Prospectivity analysis of orogenic gold deposits in Saqez-Sardasht Goldfield, Zagros Orogen, Iran

Almasi

Yousefi

Carranza

2017

Ore Geology Reviews

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Diverse deposit-types or mineral systems form by diverse geological processes, so translation of knowledge about the controls of mineralization acquired from the 4D geological modeling into 2D spatial predictor maps is a major challenge for prospectivity analysis. In this regard, mathematical functions have been used to model the conceptual or perceived spatial relationships between geological variables and targeted type or system of mineralization. In this paper, due to the different models of spatial relationships between predictors and mineral deposits, we investigated the performance of different fuzzification functions to quantify the relationships. We demonstrated that various types of relationships between exploration features and a mineralization-type sought could be quantified using different fuzzification functions for prospectivity analysis. We illustrated the process of the prospectivity analysis by using a data set of orogenic gold deposits in Saqez-Sardasht Goldfield, Iran. Prospectivity modeling of orogenic gold mineralization in the study area showed that the NE-SW trending targets have priority for further prospecting of the deposits. Saqez-Sardasht Goldfield, Zagros Orogen, Iran (by A. Almasi, M. Yousefi, E. J.M. Carranza) Manuscript submitted to Ore Geology Reviews 2 Prospectivity analysis of orogenic gold deposits in IntroductionFor mineral prospectivity mapping (MPM) of a deposit-type sought in an area, mathematical functions have been used to model spatial relationships between geological variables system of mineralization (e.g., Bonham-Carter, 1994;Luo and Dimitrakopoulos, 2003; Porwal et al., 2003a,b,c;Carranza, 2008Carranza, , 2017. In MPM, mathematical functions, have been widely used to assign weights to discretized spatial evidence values as fuzzified evidential maps in the [0,1] range or to rank target areas as fuzzy prospectivity models (e.g., Bonham-Carter, 1994;Carranza and Hale, 2002;Luo and Dimitrakopoulos, 2003;Porwal et al., 2003c;Carranza, 2008Carranza, , 2009Carranza, , 2017Lisitsin et al., 2013;Mutele et al., 2017;Nykänen et al., 2017). The weights assigned to classes of discretized evidential values may be based on (a) expert judgment directly, (b) locations of known mineral occurrences (KMOs), (c) a combination of (a) and (b), or (d) subjectively-defined functions, so indirectly-assigned by analyst (e.g., Luo, 1990;Bonham-Carter, 1994;Cheng and Agterberg, 1999;Luo and Dimitrakopoulos, 2003;Porwal et al., 2003a Porwal et al., ,b,c, 2004Porwal et al., , 2006Carranza et al., 2005;Carranza, 2008Carranza, , 2014Porwal and Kreuzer, 2010;Mejía-Herrera et al., 2014;Carranza and Laborte, 2016;McKay and Harris, 2016). All these methods impart bias due to discretization of continuous spatial values, use of subjective expert judgments, and sparse or incomplete data on locations of KMOs in knowledge-and data-driven MPM (Coolbaugh et al., 2007;Lusty et al., 2012;Ford et al., 2016).To reduce bias in the assignment of weights to continuous-value spatial evidence, various re...

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“…The accreted rocks get intensely deformed and metamorphosed, magmas are produced and cool to form granites, a mountain range is driven upward, and then the continents may rebound slightly, pulling the mountain range apart in extension. Gold may accumulate in this environment, in what are called "orogenic" gold deposits (Goldfarb et al, 2001b, Goldfarb andGroves, 2015), using a geologic term where "oro" refers to mountains, not to gold. The goldtransporting fluid, a combination of water and carbon-dioxide, was released from different minerals as they were heated and metamorphosed during the accretion.…”

Section: A Little Big History Of Iberian Goldmentioning

confidence: 99%