Three−Dimensional Metallogenic Prediction with Integration of Structural Reconstruction at the Dayingezhuang Gold Deposit, Northwestern Jiaodong Peninsula

MAO Xiancheng; WANG Chuntan; LIU Zhankun; CHEN Jin; DENG Hao; WANG Jinli

doi:10.12401/j.nwg.2023108

2023 Vol. 56, No. 5

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Article navigation > Northwestern Geology > 2023 > 56(5): 72-84

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MAO Xiancheng, WANG Chuntan, LIU Zhankun, CHEN Jin, DENG Hao, WANG Jinli. 2023. Three−Dimensional Metallogenic Prediction with Integration of Structural Reconstruction at the Dayingezhuang Gold Deposit, Northwestern Jiaodong Peninsula. Northwestern Geology, 56(5): 72-84. doi: 10.12401/j.nwg.2023108

Citation:

MAO Xiancheng, WANG Chuntan, LIU Zhankun, CHEN Jin, DENG Hao, WANG Jinli. 2023. Three−Dimensional Metallogenic Prediction with Integration of Structural Reconstruction at the Dayingezhuang Gold Deposit, Northwestern Jiaodong Peninsula. Northwestern Geology, 56(5): 72-84. doi: 10.12401/j.nwg.2023108

Three−Dimensional Metallogenic Prediction with Integration of Structural Reconstruction at the Dayingezhuang Gold Deposit, Northwestern Jiaodong Peninsula

1.
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring Ministry of Education, School of Geosciences and Info−Physics, Central South University, Changsha 410083, Hunan, China
2.
Key Laboratory of Non−Ferrous Resources and Geological Hazard Detection, Changsha 410083, Hunan, China

More Information

Corresponding author: LIU Zhankun, zkliu0322@csu.edu.cn

Received Date: 23 February 2023

Revised Date: 02 June 2023

Accepted Date: 09 June 2023

Publish Date: 20 October 2023

Abstract

Abstract

Mineral deposits are often deformed after mineralization, which is, however, less concerned in the current three−dimensional (3D) prospectivity modeling. This paper selected the Dayingezhuang structural altered rock type gold deposit as a case study and used a structural restoration method based on triangular irregular network (TIN) to reconstruct 3D orebody and ore−controlling fault, analyzed and compared the mineralization structure and ore−controlling factors before and after restoration and finally completed the 3D mineral prospectivity at depth. The results show that the structural restoration method can eliminate the variation of spatial distance and dip angle of fault and orebody caused by deformation. The reconstructed mineralization distribution has a stronger spatial autocorrelation feature that is shown as the change of scattered mineralization distribution to spatially continuous at the offset parts. In addition, the reconstructed prediction model has higher performance than that without restoration under the same parameters, indicating that the correlation between the mineralization distribution and ore control factors is more significant. Therefore, the three−dimensional metallogenic prediction modeling with integration of structural reconstruction has improved the propectivity accuracy and can provide a reliable reference for deep prospecting in the Dayingezhuang deposit.
- TIN /
- structural reconstruction /
- 3D prospectivity modeling /
- Dayingezhuang gold deposit

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References

[1]	陈进, 毛先成, 刘占坤, 等. 基于随机森林算法的大尹格庄金矿床三维成矿预测[J]. 大地构造与成矿学, 2020a, 44(02): 231-241 Google Scholar CHEN Jin, MAO Xiancheng, LIU Zhankun, et al. Three-dimensional Metallogenic Prediction Based on Random Forest Classification Algorithm for the Dayingezhuang Gold Deposit[J]. Geotectonica et Metallogenia, 2020a, 44(02): 231-241. Google Scholar
[2]	陈进, 毛先成, 邓浩. 山东大尹格庄金矿床深部三维定量成矿预测[J]. 地球学报, 2020b, 41(02): 179-191 Google Scholar CHEN Jin, MAO Xian-cheng, DENG Hao. 3D Quantitative Mineral Prediction in the Depth of the Dayingezhuang Gold Deposit, Shandong Province[J]. Acta Geoscientica Sinica, 2020b, 41(02): 179-191. Google Scholar
[3]	陈建平, 吕鹏, 吴文, 等. 基于三维可视化技术的隐伏矿体预测[J]. 地学前缘, 2007, 14(05): 54-62. Google Scholar CHEN Jianping, LV Peng, WU Wen, et al. A 3D method for predicting blind orebodies, based on a 3D visualization model and its application[J]. Earth Science Frontiers, 2007, 14(5): 054-062. Google Scholar
[4]	邓浩, 郑扬, 陈进, 等. 基于深度学习的山东大尹格庄金矿床深部三维预测模型[J]. 地球学报, 2020, 41(02): 157-165 doi: 10.3975/cagsb.2020.020501 CrossRef Google Scholar DENG Hao, ZHENG Yang, CHEN Jin, et al. Deep Learning-based 3D Prediction Model for the Dayingezhuang Gold Deposit, Shandong Province[J]. Acta Geoscientica Sinica, 2020, 41(02): 157-165. doi: 10.3975/cagsb.2020.020501 CrossRef Google Scholar
[5]	邓浩, 魏运凤, 陈进, 等. 基于注意力卷积神经网络的焦家金矿带三维成矿预测及构造控矿因素定量分析[J]. 中南大学学报(自然科学版), 2021, 52(09): 3003-3014 Google Scholar DENG Hao, WEI Yunfeng, CHEN Jin, et al. Three-dimensional prospectivity mapping and quantitative analysis of structural ore-controlling factors in Jiaojia Au ore-belt with attention convolutional neural networks[J]. Journal of Central South University(Science and Technology), 2021, 52(09): 3003-3014. Google Scholar
[6]	李洪奎, 禚传源, 耿科, 等. 郯-庐断裂带陆内伸展构造: 以沂沭断裂带的表现特征为例[J]. 地学前缘, 2017, 24(02): 73-84 Google Scholar LI Hongkui, ZHOU Chuanyuan, GENG Ke, et al. Intra-continental extensional tectonics of the Tan-Lu fault zone: an example from the appearance characteristics of the Yishu fault zone[J]. Earth Science Frontiers, 2017, 24(02): 73-84. Google Scholar
[7]	毛先成, 王琪, 陈进, 等. 胶西北金矿集区深部成矿构造三维建模与找矿意义[J]. 地球学报, 2020, 41(02): 166-178 doi: 10.3975/cagsb.2020.020702 CrossRef Google Scholar MAO Xian-cheng, WANG Qi, CHEN Jin, et al. Three-dimensional Modeling of Deep Metallogenic Structure in Northwestern Jiaodong Peninsula and Its Gold Prospecting Significance[J]. Acta Geoscientica Sinica, 2020, 41(02): 166-178. doi: 10.3975/cagsb.2020.020702 CrossRef Google Scholar
[8]	邱芹军, 马凯, 朱恒华, 等. 基于BERT的三维地质建模约束信息抽取方法及意义. 西北地质, 2022, 55(4): 124−132. Google Scholar QIU Qinjun, MA Kai, ZHU Henghua, et al. BERT-based Method and Significance of Constraint Information Extraction for 3D Geological Modelling. Northwestern Geology, 2022, 55(4): 124-132. Google Scholar
[9]	宋明春, 李杰, 李世勇, 等. 鲁东晚中生代热隆-伸展构造及其动力学背景[J]. 吉林大学学报(地球科学版), 2018, 48(04): 941-964 Google Scholar SONG Mingchun, LI Jie, LI Shiyong, et al. Late Mesozoic Thermal Upwelling-Extension Structure and Its Dynamics Background in Eastern Shandong Province[J]. Journal of Jilin University(Earth Science Edition), 2018, 48(04): 941-964. Google Scholar
[10]	肖克炎, 李楠, 孙莉, 等. 基于三维信息技术大比例尺三维立体矿产预测方法及途径[J]. 地质学刊, 2012, 36(03): 229-236 doi: 10.3969/j.issn.1674-3636.2012.03.229 CrossRef Google Scholar XIAO Ke-yan, LI Nan, SUN Li, et al. Large scale 3D mineral prediction methods and channels based on 3D information technology[J]. Journal of Geology, 2012, 36(03): 229-236. doi: 10.3969/j.issn.1674-3636.2012.03.229 CrossRef Google Scholar
[11]	徐述平, 杨立强, 张蜀冀, 等. 胶东招平断裂带金矿成矿指示元素特征及找矿应用[J]. 黄金科学技术, 2010, 18(05): 7-11 doi: 10.3969/j.issn.1005-2518.2010.05.002 CrossRef Google Scholar XU Shuping, YANG Liqiang, ZHANG Shuji, et al. Metallogenic Indication Element Characteristics and Application of Gold De-posit in Zhaoyuan-Pingdu Fault Zone[J]. Gold Science and Technology, 2010, 18(05): 7-11. doi: 10.3969/j.issn.1005-2518.2010.05.002 CrossRef Google Scholar
[12]	杨立强, 邓军, 王中亮, 等. 胶东中生代金成矿系统[J]. 岩石学报, 2014, 30(09): 2447-2467 Google Scholar YANG Liqiang, DENG Jun, WANG Zhongliang, et al. Mesozoic gold metallogenic system of the Jiaodong gold province, eastern China[J]. Acta Petrologica Sinica, 2014, 30(9): 2447-2467. Google Scholar
[13]	赵鹏大. 成矿定量预测与深部找矿[J]. 地学前缘, 2007, 14(05): 1-10 doi: 10.3321/j.issn:1005-2321.2007.05.001 CrossRef Google Scholar ZHAO Pengda. Quantitative miniral prediction and deep mineral exploration[J]. Earth Science Frontiers, 2007, 14(05): 1-10. doi: 10.3321/j.issn:1005-2321.2007.05.001 CrossRef Google Scholar
[14]	Bray E, John D, Cousens B. Petrologic, tectonic, and metallogenic evolution of the southern segment of the ancestral Cascades magmatic arc, California and Nevada[J]. Geosphere, 2014, 10(1): 1-39. doi: 10.1130/GES00944.1 CrossRef Google Scholar
[15]	Chen J, Jiang L, Peng C, et al. Quantitative resource assessment of hydrothermal gold deposits based on 3D geological modeling and improved volume method: Application in the Jiaodong gold Province, Eastern China[J]. Ore Geology Reviews, 2022, 105282. Google Scholar
[16]	Deng J, Yang L, Li R, et al. Regional structural control on the distribution of world-class gold deposits: An overview from the Giant Jiaodong Gold Province, China[J]. Geological Journal, 2019, 54: 378-391. doi: 10.1002/gj.3186 CrossRef Google Scholar
[17]	Hronsky J M A. Deposit-scale structural controls on orogenic gold deposits: an integrated, physical process-based hypothesis and practical targeting implications[J]. Mineralium Deposita, 2019, 55(2): 197-216. Google Scholar
[18]	Huston D L, Blewett R S, Champion D C. Australia through time: A summary of its tectonic and metallogenic evolution[J]. Episodes, 2012, 35(1): 23-43. doi: 10.18814/epiiugs/2012/v35i1/004 CrossRef Google Scholar
[19]	Jiang N, Guo J, Fan W, et al. Archean TTGs and sanukitoids from the Jiaobei terrain, North China craton: Insights into crustal growth and mantle metasomatism[J]. Precambrian Research, 2016, 281: 656-672. doi: 10.1016/j.precamres.2016.06.019 CrossRef Google Scholar
[20]	Lebrun E, Miller J, Thebaud N, et al. Structural Controls on an Orogenic Gold System: The World-Class Siguiri Gold District, Siguiri Basin, Guinea, West Africa[J]. Economic Geology, 2017, 112(1): 73-89. doi: 10.2113/econgeo.112.1.73 CrossRef Google Scholar
[21]	Liu L M, Peng S L. Key strategies for predictive exploration in mature environment: model innovation, exploration technology optimization and information integration[J]. Journal of Central South University of Technology(English Edition), 2005, (02): 186-191. Google Scholar
[22]	Liu Z K, Mao X C, Wang F Y, et al. Deciphering anomalous Ag enrichment recorded by galena in Dayingezhuang Au (-Ag) deposit, Jiaodong Peninsula, Eastern China[J]. Transactions of Nonferrous Metals Society of China, 2021a, 31(12): 3831-3846. doi: 10.1016/S1003-6326(21)65768-0 CrossRef Google Scholar
[23]	Liu Z K, Chen J, Mao X C, et al. Spatial Association Between Orogenic Gold Mineralization and Structures Revealed by 3D Prospectivity Modeling: A Case Study of the Xiadian Gold Deposit, Jiaodong Peninsula, China[J]. Natural Resources Research, 2021b, 30: 3987-4007. doi: 10.1007/s11053-021-09956-9 CrossRef Google Scholar
[24]	Lu Y, Liu L, Xu G. Constraints of deep crustal structures on large deposits in the Cloncurry district, Australia: Evidence from spatial analysis[J]. Ore Geology Reviews, 2016, 79: 316-331. doi: 10.1016/j.oregeorev.2016.05.022 CrossRef Google Scholar
[25]	Mao X, Ren J, Liu Z, et al. Three-dimensional prospectivity modeling of the Jiaojia-type gold deposit, Jiaodong Peninsula, Eastern China: A case study of the Dayingezhuang deposit[J]. Journal of Geochemical Exploration, 2019, 203: 27-44. doi: 10.1016/j.gexplo.2019.04.002 CrossRef Google Scholar
[26]	Song M, Li S, Santosh M, et al. Types, characteristics and metallogenesis of gold deposits in the Jiaodong Peninsula, Eastern North China Craton[J]. Ore Geology Reviews, 2015, 65: 612-625. doi: 10.1016/j.oregeorev.2014.06.019 CrossRef Google Scholar
[27]	Wang J, Mao X, Peng C, et al. Three-Dimensional Refined Modelling of Deep Structures by Using the Level Set Method: Application to the Zhaoping Detachment Fault, Jiaodong Peninsula, China[J]. Mathematical Geosciences, 2022, 55(2): 229-262. Google Scholar
[28]	Xie S, Xie H, Wang S, et al. Ca. 2. 9 Ga granitoid magmatism in eastern Shandong, North China Craton: Zircon dating, Hf-in-zircon isotopic analysis and whole-rock geochemistry[J]. Precambrian Research, 2014, 255: 538-562. doi: 10.1016/j.precamres.2014.09.006 CrossRef Google Scholar
[29]	Yang L Q, Deng J, Goldfarb R J, et al. 40Ar/39Ar geochronological constraints on the formation of the Dayingezhuang gold deposit: New implications for timing and duration of hydrothermal activity in the Jiaodong gold province, China. Gondwana Research, 2014, 25(4): 1469-1483. Google Scholar
[30]	Yang L Q, Deng J, Wang Z L, et al. Relationships between gold and pyrite at the Xincheng gold deposit, Jiaodong Peninsula, China: Implications for gold source and deposition in a brittle epizonal environment[J]. Economic Geology, 2016, 111: 105-126. doi: 10.2113/econgeo.111.1.105 CrossRef Google Scholar
[31]	Yu S Y, Deng H, Liu Z K, et al. Identifying multivariate geochemical anomalies via tensor dictionary learning over spatial-elemental dimensionalities[J]. Computers & Geosciences, 2022, 165: 105153. Google Scholar
[32]	Yu X, Shan W, Xiong Y, et al. Deep structural framework and genetic analysis of gold concentration areas in the Northwestern Jiaodong Peninsula, China: A new understanding based on high-resolution reflective seismic survey[J]. Acta Geol Sin-Engl, 2018, 92(5): 1823-1840. doi: 10.1111/1755-6724.13679 CrossRef Google Scholar
[33]	Zuo R, Carranza E. Support vector machine: A tool for mapping mineral prospectivity[J]. Computers&Geosciences, 2011, 37(12): 1967-1975. Google Scholar