Hydrodynamic Links between Shallow and Deep Mineralization Systems and Implications for Deep Mineral Exploration

Chi, Guoxiang; Xu, Deru; Xue, Chunji; LI, Zenghua; Ledru, Patrick; Deng, Teng; WANG, Yumeng; Song, Hao

doi:10.1111/1755-6724.14903

Cited by 26 publications

(3 citation statements)

References 159 publications

(299 reference statements)

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“…As ELIP activity reached its peak, the intrusion of magma intensified deep-seated hydrothermal activity in the study area, and the exceptionally high thermal effect and pressure provided the driving force for the upward movement of deep hydrothermal fluids [70]. Deep faults and their associated fracture systems provided preferential pathways for the upward migration of hydrothermal fluids.…”

Section: Genetic Mechanism Of the Dolomitesmentioning

confidence: 94%

Genetic Mechanism of Structurally Controlled Dolomites Derived from Seawater-Hydrothermal Mixed Fluids—A Case Study from Middle Permian, Central Sichuan Basin, South China

et al. 2023

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Dolomite bodies in the Middle Permian of the central Sichuan Basin have been reported as favorable natural gas reservoirs. The Middle Permian dolomite consists of three types of recrystallized dolomite (Rd1, Rd2, and Rd3) and one type of dolomite cement (Sd). Rd1 might be formed as the primary mineral along the calcite in the original sea-water. Its δ13C value and 87Sr/86Sr ratio, consistent with those of marine limestone and Permian seawater, support that the dolomitizing fluid for Rd1 was Permian seawater preserved in the strata. Rd2 consists of fine to medium (50 μm to 250 μm) and planar to curved crystals. Geochemical indicators (slightly high 87Sr/86Sr ratio, similar rare earth element patterns, negative δ18O, slightly high salinity) confirm that the dolomitizing fluid of Rd2 was mainly Permian seawater during shallow burial, with a small number of hydrothermal fluids. Rd3 and Sd are featured by very large (>250 μm), curved crystals, and high-temperature, high-salinity, and obviously positive Eu anomalies, suggesting that their diagenetic fluids were mainly hydrothermal fluids from deep. Additionally, inherited carbon sources and the 87Sr/86Sr ratios of some samples fall within the range of Permian seawater distribution, confirming the contribution of Permian seawater. ELIP activity caused the formation of this dolomite through the mixing of seawater and hydrothermal fluids. The main fluid circulation channels were activated basement faults, epigenetic karst pores, and shallowly buried high-permeability strata. During the peak period of ELIP activity, the continuous upwelling of deep hydrothermal fluids led to the continuous formation of Rd2, Rd3, and Sd. The dolomitization fluid of Rd2 was mainly composed of seawater and featured a certain lateral extension, which was away from faults. Rd3 and Sd are mainly distributed along the fault system, and excessive dolomitization caused by the hydrothermal activity, to some extent, inhibited the lateral movement of hydrothermal fluids. This study provides a good example for exploring the genetic mechanism and distribution pattern of structurally controlled dolomites under a volcanic activity background.

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Section: Genetic Mechanism Of the Dolomitesmentioning

confidence: 94%

Genetic Mechanism of Structurally Controlled Dolomites Derived from Seawater-Hydrothermal Mixed Fluids—A Case Study from Middle Permian, Central Sichuan Basin, South China

et al. 2023

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“…Considering the connectivity of percolation networks in hydrothermal mineral systems, it is assumed that zones of higher geological complexity and areas where faults and shear zones are developed represent major pathways with a higher probability of percolation network connectivity (Hodkiewicz et al, 2005;Chi et al, 2022). Lineament density was applied here to study the influence of faults and lithological contacts on the occurrences of Au deposits.…”

Section: Lineament Densitymentioning

confidence: 99%

Enhancing Geological Understanding and Identifying Gold Anomalies in the Ailaoshan Orogen

ZHAO,

YANG,

SONG

et al. 2024

Acta Geologica Sinica (Eng)

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The Ailaoshan Orogen, situated between the Yangtze and Simao Blocks, underwent a complex structural, magmatic, and metamorphic evolution resulting in different tectonic subzones with varying structural lineaments and elemental concentrations. These can conceal or reduce anomalies due to the mutual effect between different anomaly areas. This study divided the whole zone into subzones based on tectonic settings, ore‐cluster areas, or sample catchment basins (SCB). To identify geochemical and structural anomalies associated with gold mineralization, the mean plus twice standard deviations (Mean+2STD), factor analysis (FA), concentration–area (C‐A) modeling of stream sediment geochemical data, and lineament density were utilized in both the Ailaoshan Orogen and the individual subzones. The FA in the divided 98 SCBs with 6 SCBs containing gold deposits can roughly ascertain unknown rock types, identify specific element associations of known rocks and discern the porphyry or skarn‐type gold mineralization. Compared with methods of Mean + 2STD and C–A model of data in the whole Ailaoshan Orogen, which mistake the anomalies as background or act the background as anomalies, the combined methods of FA and C‐A in the separate subzones or SCBs works well in regional metallogenic potential analysis. Mapping of lineament densities with a 10 km circle diameter is not suitable to locate gold deposits due to the delineated large areas of medium‐high lineament density. In contrast, the use of circle diameters of 1.3 km or 1.7 km in the ore‐cluster scale delineates areas with a higher concentration of lineament density, consistent with the locations of known gold deposits. By analyzing the map of faults and gold anomalies, two potential prospecting targets, SCBs 1 and 63 with a sandstone as a potential host rock for Au, were identified in the Ailaoshan Orogen. The use of combined methods in the divided subzones proved to be more effective in improving geological understanding and identifying mineralization anomalies associated with Au, rather than analyzing the entire large area.

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“…which regards the spatial information as multivariate features [15,36,39] Unstructured data was adeptly processed using Artificial Neural Network Model algorithms to unveil the obscured relationships between structure and mineralization. [43][44][45][46] The Multilayer Perceptron (MLP), a widely implemented artificial neural network model, is adept at addressing both classification and regression challenges. It comprises multiple layers of neurons, including an input layer, one or more hidden layers, and an output layer.…”

Section: Mineral Prospectivity Modelingmentioning

confidence: 99%

3D Prospectivity Modeling of the Nibao Carlin-Type Gold Deposit, China: Insights into Mineralization Localization and Gold Exploration

Guo,

Mao,

Huang

2024

Preprint

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Existing research indicates that mineralization of Carlin-type gold deposit is related to geological structures, but current studies mostly focus on the qualitative association, and lack quantitative analysis of the relationship between mineralization and structural features, which limits the potential for predictive mineral exploration. In this study, the Nibao Carlin-type gold deposit was selected as the research area to conduct three-dimensional geological modeling, spatial analysis, and three-dimensional prospectivity modeling. Spatial analysis revealed that the majority of gold ore bodies are situated near the main fault, with gold mineralization primarily occurring within a spatial range of approximately -80 meters to 20 meters from the main fault plane. This spatial distribution predominantly aligns in a SW-NE direction. Spatial characteristic extraction of fault slope indicators suggests that the slope angles are conducive to the migration and accumulation of ore-forming hydrothermal fluids, with ore body metal content rapidly increasing within an angle range of approximately 20° to 60°. The shape analysis of the mainfault indicates that the principal spaces of mineralization enrichment are within -10m to 20m of the fault plane undulations, where gold resources are concentrated in a relatively flat area. However, the degree of mineralization is more pronounced in the protruding sections than in the depressed areas. The areas with significant dip changes in faults display higher variations in orebody attitudes, with mineralization noticeably weaker in areas where the fault steepness changes markedly. Ore-bearing units exhibit significantly higher grades within the -17° to 3° interval of fault steepness transition. This interval also sees a rapid accumulation of gold resources. The Nibao gold mine's mineralization is derived from deep, ore-bearing hydrothermal fluids ascending along major fractures, precipitating, and accumulating in favorable ore-hosting spaces (faults, anticline cores) and lithological combinations (sedimentary tuffs, tuffs). Three deep potential gold targets were identified, highlighting the deep western edge of the F1 fault and the deep southeast-trending lower plate as prospective areas for future exploration. This study underscores the effectiveness of 3D prospective modeling as an indispensable tool for elucidating the spatial distribution of fault-controlled Carlin-type gold deposits and their tectonic determinants.

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Hydrodynamic Links between Shallow and Deep Mineralization Systems and Implications for Deep Mineral Exploration

Cited by 26 publications

References 159 publications

Genetic Mechanism of Structurally Controlled Dolomites Derived from Seawater-Hydrothermal Mixed Fluids—A Case Study from Middle Permian, Central Sichuan Basin, South China

Genetic Mechanism of Structurally Controlled Dolomites Derived from Seawater-Hydrothermal Mixed Fluids—A Case Study from Middle Permian, Central Sichuan Basin, South China

Enhancing Geological Understanding and Identifying Gold Anomalies in the Ailaoshan Orogen

3D Prospectivity Modeling of the Nibao Carlin-Type Gold Deposit, China: Insights into Mineralization Localization and Gold Exploration

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