Geological and Geochemical Characteristics of the Archean Basement-Hosted Gold Deposit in Pinglidian, Jiaodong Peninsula, Eastern China: Constraints on Auriferous Quartz-Vein Exploration

Abstract

The gold deposits that are hosted in the Archean metamorphic rock, have yet to be explored beyond Pinglidian gold deposit in the northwestern Jiaodong Peninsula, eastern China. This kind of gold deposit differs from those that are hosted in Mesozoic granitoids, showing good potential for the prospecting of auriferous quartz-vein gold deposits controlled by the structures in greenfield Archean metamorphic rock. Pinglidian gold deposit is located in the hanging wall of the Jiaojia fault and consists of eight separated orebodies that are enveloped by altered rock in Archean biotite plagiogneiss. These orebodies and wall-rock alterations are strongly controlled by local structures that formed during the Mesozoic rotation and kink folding of the foliated and fissile Archean basement host. The major wall-rock alterations comprise sericitization, silicification, pyritization, and carbonation, which is up to 18 m in width and progressively increases in intensity towards the auriferous quartz vein. The visible gold is present as discrete native gold and electrum grains, which have basically filled in all manner of fractures or are adjacent to galena. We recognize two types of gold bearing quartz veins that are associated with mineral paragenetic sequences during hydrothermal alteration in the Pinglidian gold deposit. The petrological features and geochemical compositions in the reaction fronts of the alteration zone suggest variations in the physicochemical conditions during ore formation. These minerals in the wall rock, such as plagioclase, biotite, zircon, titanite, and magnetite, have been broken down to hydrothermal albite, sericite, and quartz in a K–Na–Al–Si–O–H system, and sulfides in a Fe–S–O–H system. The major and trace elements were calculated by the mass-balance method, showing gains during early alteration and losses during late alteration. The contents of K2O, Na2O, CaO, and LOI varied within the K–Na–Al–Si–O–H system during alteration, while Fe2O3 and MgO were relatively stable. Rare-earth elements (REE) changed from gains to losses alongside the breakdown of accessory minerals, such as large ion lithophile elements (LILE). The Sr and Ba contents exhibited high mobility during sericite-quartz alteration. Most of the low-mobility high-field strength elements (HFSE) were moderately depleted, except for Pb, which was extremely high in anomalous samples. The behavior of trans-transition elements (TRTE) was related to complicated sulfides in the Fe–S–O–H system and was constrained by the parameters of the mineral assemblages and geochemical compositions, temperature, pressure, pH, and fO2. These factors during ore formation that were associated with the extents and intensity of sulfide alteration and gold precipitation can be utilized to evaluate the potential size and scale of an ore-forming hydrothermal system, and is an effective exploration tool for widespread auriferous quartz veins in Archean metamorphic basements.

Auriferous Quartz Veining Due to CO2 Content Variations and Decompressional Cooling, Revealed by Quartz Solubility, SEM-CL and Fluid Inclusion Analyses (The Linglong Goldfield, Jiaodong)

“…Gold deposits in Jiaodong are structurally controlled by a set of NE-to NNE-trending fault systems [53], forming three clusters, from west to east, namely the Zhaoyuan-Laizhou, Penglai-Qixia and Muping-Rushan belts [54]. Over 95% of the gold resources are hosted in the Jurassic to Cretaceous granitoids [55], with only a few small-sized deposits being hosted in the metamorphic basement [56,57]. Gold occurrence occurs either as auriferous quartz veins (Linglong-type), or as disseminated/stockwork altered rocks (Jiaojia-type) [29] (Figure 1a).…”

An Enhanced Rock Mineral Recognition Method Integrating a Deep Learning Model and Clustering Algorithm

“…Moreover, gray is the mean value of the grayscale of the central pixel and 8 pixels around the central pixel. In order to eliminate edge influence, the maximum and minimum are removed, as shown in Equations (5) and (6).…”

“…The traditional recognition methods are based on the physical and chemical properties of rock minerals, which are used to identify rock minerals at macro-and micro-scales [1,2]. With the development of computer science and artificial intelligence, the recognition model can be established using machine learning methods [3][4][5][6][7]. Traditional recognition methods have definite physical meaning; while machine learning methods are driven by data [8,9].…”

Geochemistry, Zircon U–Pb Geochronology, and Lu–Hf Isotopes of the Chishan Alkaline Complex, Western Shandong, China

“…Tectonically, this complex belongs to the Luxi Block at the southeastern margin of the North China Craton and the west side of the Tanlu fault zone (Figure 1a) [28,29]. The Luxi Block is bounded by the Tanlu fault zone to the east, the Liaocheng-Lankao fault zone to the west, the south of the Qihe-Guangrao fault to the north, and the Fengpei fault to the south (Figure 1b) [36][37][38][39][40][41][42][43][44][45]. The exposed formations in the study area include the Neoarchean Taishan rock group, Paleozoic Cambrian to Permian carbonate and clastic rocks, Mesozoic Triassic to Cretaceous clastic and volcanic rocks, and Cenozoic clastic rocks (Figure 1b), with the Neoarchean granodiorite constituting the crystalline basement.…”