The Ore-forming Fluid of the Gold Deposits of Muru Gold Belt in Eastern Shandong, China - a Case Study of Denggezhuang Gold Deposit

Abstract. Denggezhuang gold deposit is an epithermal gold‐quartz vein deposit in northern Muru gold belt, eastern Shandong, China. The deposit occurs in the NNE‐striking faults within the Mesozoic granite. The deposit consists of four major veins with a general NNE‐strike. Based on crosscutting relationships and mineral parageneses, the veins appear to have been formed during the same mineralization epochs, and are further divided into three stages: (1) massive barren quartz veins; (2) quartz‐sulfides veins; (3) late, pure quartz or calcite veinlets. Most gold mineralization is associated with the second stage. The early stage is characterized by quartz, and small amounts of ore minerals (pyrite), the second stage is characterized by large amounts of ore minerals. Fluid inclusions in vein quartz contain C‐H‐O fluids of variable compositions. Three main types of fluid inclusions are recognized at room temperature: type I, two‐phase, aqueous vapor and an aqueous liquid phase (L+V); type II, aqueous‐carbonic inclusions, a CC₂‐liquid with/without vapor and aqueous liquid (LCO₂+VCC₂+Laq.); type III, mono‐phase aqueous liquid (Laq.). Data from fluid inclusion distribution, microthermometry, and gas analysis indicate that fluids associated with Au mineralized quartz veins (stage 2) have moderate salinity ranging from 1.91 to 16.43 wt% NaCl equivalent (modeled salinity around 8–10 wt% NaCl equiv.). These veins formatted at temperatures from 80d? to 280d?C. Fluids associated with barren quartz veins (stage 3) have a low salinity of about 1.91 to 2.57 wt% NaCl equivalent and lower temperature. There is evidence of fluid immiscibility and boiling in ore‐forming stages. Stable isotope analyses of quartz indicate that the veins were deposited by waters with δ^O and δD values ranging from those of magmatic water to typical meteoric water. The gold metallogenesis of Muru gold belt has no relationship with the granite, and formed during the late stage of the crust thinning of North China.     

Bayesian Spatial Modelling of Gamma Ray Count Data

Gamma ray logging is a method routinely employed by geophysicists and environmental engineers in site geology evaluations. Modelling of gamma ray data from individual boreholes assists in the local identification of major lithological changes; modelling these data from a network of boreholes assists with lithological mapping and spatial stratigraphic correlation. In this paper we employ Bayesian spatial partition models to analyse gamma ray data spatially. In particular, a spatial partition is defined via a Voronoi tessellation and the mean intensity is assumed constant in each cell of the partition. The number of vertices generating the tessellation as well as the locations of vertices are assumed unknown, and uncertainty about these quantities is described via a hierarchical prior distribution. We describe the advantages of the spatial partition modelling approach in the context of smoothing gamma ray count data and describe an implementation that may be extended to the fitting of a more general model than a constant mean within each cell of the partition. As an illustration of the methodology we consider a data set collected from a network of eight boreholes, which is part of a geophysical study to assist in mapping the lithology of a site. Gamma ray logs are linked with geological information from cores and the spatial analysis of log data assists with predicting the lithology at unsampled locations.