Three-dimensional prospectivity modeling of the Jiaojia-type gold deposit, Jiaodong Peninsula, Eastern China: A case study of the Dayingezhuang deposit

Mao, Xiaoming; Ren, Junxue; Liu, Zhankun; Chen, Jin; Tang, Lei; Deng, Hao; Bayless, Richard C.; Yang, Bin; Mijun, Wang; Liu, Chunming

doi:10.1016/j.gexplo.2019.04.002

Cited by 77 publications

(48 citation statements)

References 79 publications

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“…The deep nature of this exploration means that only certain types of exploration tools can be used, such as geophysics and drilling based on structural and geophysical targeting [46][47][48][49][50]. Recent advances in three dimensional prospectivity modeling for mineral exploration has highlighted the potential use of prospectivity modeling [12][13][14][15][16] and numerical modeling [21] in this type of exploration targeting. However, the simulation undertaken during this study provides another method of predicting the location of areas that are prospective for deep exploration, supplementing more traditional exploration approaches, and removing risks related to the conditional dependence associated with some other types of prospectivity modeling.…”

Section: Discussionmentioning

confidence: 99%

“…Rapid recent advancements in computing hardware and theoretical and applied computational science have enabled the development of prospectivity modeling [12][13][14][15][16] and the complex coupled numerical simulation of geological processes [17][18][19][20][21]. However, typical three-dimensional prospectivity modeling workflows (e.g., weights-of-evidence approaches) frequently encounter issues of conditional dependence, where datasets are biased by relationships where given exploration criteria can generate responses in different datasets, particular mineralizing processes can generate more than one exploration criteria, or responses present within one dataset can be conditioned by responses in another dataset [12].…”

Section: Of 19mentioning

confidence: 99%

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Numerical Simulation Based Targeting of the Magushan Skarn Cu–Mo Deposit, Middle-Lower Yangtze Metallogenic Belt, China

Yuan

et al. 2019

Minerals

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The Magushan Cu–Mo deposit is a skarn deposit within the Nanling–Xuancheng mining district of the Middle-Lower Yangtze River Metallogenic Belt (MLYRMB), China. This study presents the results of a new numerical simulation that models the ore-forming processes that generated the Magushan deposit and enables the identification of unexplored areas that have significant exploration potential under areas covered by thick sedimentary sequences that cannot be easily explored using traditional methods. This study outlines the practical value of numerical simulation in determining the processes that operate during mineral deposit formation and how this knowledge can be used to enhance exploration targeting in areas of known mineralization. Our simulation also links multiple subdisciplines such as heat transfer, pressure, fluid flow, chemical reactions, and material migration. Our simulation allows the modeling of the formation and distribution of garnet, a gangue mineral commonly found within skarn deposits (including within the Magushan deposit). The modeled distribution of garnet matches the distribution of known mineralization as well as delineating areas that may well contain high garnet abundances within and around a concealed intrusion, indicating this area should be considered a prospective target during future mineral exploration. Overall, our study indicates that this type of numerical simulation-based approach to prospectivity modeling is both effective and economical and should be considered an additional tool for future mineral exploration to reduce exploration risks when targeting mineralization in areas with thick and unprospective sedimentary cover sequences.

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Section: Discussionmentioning

confidence: 99%

Section: Of 19mentioning

confidence: 99%

Numerical Simulation Based Targeting of the Magushan Skarn Cu–Mo Deposit, Middle-Lower Yangtze Metallogenic Belt, China

Yuan

et al. 2019

Minerals

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“…The deep nature of this exploration means that only certain types of exploration tools can be used, such as geophysics and drilling based on structural and geophysical targeting [44][45][46][47][48]. Recent advances in 3D prospectivity modeling have highlighted the potential use of prospectivity [12][13][14][15][16] and numerical modeling [21] in exploration targeting. However, the simulation undertaken during this study provides an additional approach to identifying areas prospective for deep-seated exploration purely based on 3D numerical simulation, supplementing (although not replacing given the reliance of this approach on, e.g., geophysical data) more traditional exploration approaches and removing some of the risks conditional dependence-related risks associated with other types of prospectivity modeling.…”

Section: Discussionmentioning

confidence: 99%

“…Rapid recent advancements in computing hardware and theoretical and applied computational science have enabled the development of three-dimensional prospectivity modeling (e.g., [12][13][14][15][16][17][18]) and the complex coupled numerical simulation of physical processes [19][20][21][22][23], including those involved in ore formation and mineralizing systems. However, traditional three-dimensional prospectivity modeling approaches (e.g., weights-of-evidence) frequently encounter issues related to conditional independence, where datasets are biased by relationships where given exploration criteria can generate responses in different datasets, particular mineralizing processes can generate more than one exploration criteria, or responses present within one dataset can be conditioned by responses in another dataset [15].…”

Section: Introductionmentioning

confidence: 99%

3D Numerical Simulation-Based Targeting of Skarn Type Mineralization within the Xuancheng-Magushan Orefield, Middle-Lower Yangtze Metallogenic Belt, China

Yuan

et al. 2020

Lithosphere

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Recent exploration has identified a series of Cu-Mo skarn deposits within the Xuancheng-Magushan orefield. The orefield forms part of the Nanling-Xuancheng mining district, which is located within the Middle-Lower Yangtze River Metallogenic Belt (MLYRMB) of central-eastern China. However, this area contains thick and widespread unprospective sedimentary cover sequences that have impeded traditional approaches to mineral exploration. This study presents the results of 3D numerical simulation modeling that identifies possible mineral exploration targets within the entire Xuancheng-Magushan orefield. This modeling enables the identification of unexplored areas with significant exploration potential that are covered by thick sedimentary sequences that cannot be easily explored using traditional exploration approaches. This study outlines the practical value of 3D numerical simulation-based targeting in areas with thick sedimentary cover sequences and uses the Flac3D software package to couple processes involved in ore formation such as stress, pressure, and heat transfer. Here, we use volumetric strain increments calculated during numerical modeling as the thermodynamic representation of the generation of space during prograde skarn formation, with this space filed by sulfides either penecontemporaneously or soon after magmatism. This process occurred during retrograde hydrothermal ore formation and the genesis of the skarn-type mineralization in this area. The results of the volumetric strain increment calculated during this numerical modeling study matches the distribution of known mineralization as well as delineating eight potential targets that have not yet been explored but represent areas of significant exploration potential within the Xuancheng-Magushan orefield, indicating these targets should be considered prospective for future mineral exploration. One of these targets was also identified during our previous Comsol-based numerical modeling of the formation of the Magushan Cu-Mo skarn deposit. The fact that this area has been identified as prospective using two different numerical modeling methods indicates that this area should be prioritized for future exploration and also validates the numerical modeling approaches used here and in our previous research that more specifically focused on the Magushan skarn deposit. Overall, our study indicates that prospectivity modeling using 3D numerical simulation-based approaches can be both effective and economical and should be considered an additional tool for future mineral exploration to reduce exploration risks when targeting mineralization in areas with thick and unprospective sedimentary cover sequences.

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“…3D spatial analysis has been demonstrated as an effective approach for identifying the spatial features of geological objects in 3D space and extracting diverse potential ore-controlling features for 3D prospectivity modeling [17][18][19][20][21][22]. However, the spatial association between ore and ore-controlling features would be significantly more complicated with the expansion of geological features in space [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

3D Mineral Prospectivity Modeling for the Low-Sulfidation Epithermal Gold Deposit: A Case Study of the Axi Gold Deposit, Western Tianshan, NW China

et al. 2020

Self Cite

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The Axi low-sulfidation (LS) epithermal deposit in northwestern China is the result of geological controls on hydrothermal fluid flow through strike-slip faults. Such controls occur commonly in LS epithermal deposits worldwide, but unfortunately, these have not been quantitatively analyzed to determine their spatial relationships with gold distribution and further guide mineral prospecting. In this study, we conduct a 3D mineral prospectivity modeling approach for the Axi deposit involving 3D geological modeling, 3D spatial analysis, and prospectivity modeling. The spatial analysis of geometric features revealed the gold mineralization trends in convex segments (0–20 m) with a specific distance from fault 2, the lower interface of late volcanic phase, and the upper interface of phyllic alteration with steep slopes (>65°), implying that gold deposition was significantly controlled by the morphological characteristics and distance fields of geologic features. The present alteration–mineralization zone at Axi has a larger width in bending sites (sections No. 35–15 and No. 40–56) than elsewhere, indicating the location of two fluid conduits extending to depth. The prediction-area plots and receiver operating characteristic curves demonstrated that (genetic algorithm optimized support vector regression (GA-SVR)) outperformed multiple nonlinear regression and fuzzy weights-of-evidence, which was proposed as a robust method to solve complicated nonlinear and high-dimensional issues in prospectivity modeling. Our study manifests spatial controls of structure, host rock, and alteration on LS epithermal gold deposition, and highlights the capability of GA-SVR for identifying deposit-scale potential epithermal gold mineralization.

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Three-dimensional prospectivity modeling of the Jiaojia-type gold deposit, Jiaodong Peninsula, Eastern China: A case study of the Dayingezhuang deposit

Cited by 77 publications

References 79 publications

Numerical Simulation Based Targeting of the Magushan Skarn Cu–Mo Deposit, Middle-Lower Yangtze Metallogenic Belt, China

Numerical Simulation Based Targeting of the Magushan Skarn Cu–Mo Deposit, Middle-Lower Yangtze Metallogenic Belt, China

3D Numerical Simulation-Based Targeting of Skarn Type Mineralization within the Xuancheng-Magushan Orefield, Middle-Lower Yangtze Metallogenic Belt, China

3D Mineral Prospectivity Modeling for the Low-Sulfidation Epithermal Gold Deposit: A Case Study of the Axi Gold Deposit, Western Tianshan, NW China

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