地学前缘 ›› 2024, Vol. 31 ›› Issue (4): 58-72.DOI: 10.13745/j.esf.sf.2024.5.13

• 大数据算法与图形大数据 • 上一篇    下一篇

基于大数据关联规则算法的卡林型金矿床元素富集规律及找矿方法研究

曹胜桃1,2(), 胡瑞忠1,2,*(), 周永章3,*(), 刘建中4,5, 谭亲平1, 高伟1, 郑禄林5, 郑禄璟5, 宋威方5   

  1. 1.中国科学院 地球化学研究所 矿床地球化学国家重点实验室, 贵州 贵阳 550081
    2.中国科学院大学 地球与行星科学学院, 北京 100049
    3.中山大学 地球环境与地球资源研究中心, 广东 广州 510275
    4.贵州省地质矿产勘查开发局, 贵州 贵阳 550004
    5.贵州大学, 贵州 贵阳 550025
  • 收稿日期:2024-02-26 修回日期:2024-04-29 出版日期:2024-07-25 发布日期:2024-07-10
  • 通信作者: * 胡瑞忠(1958—),男,研究员,中国科学院院士,主要从事矿床学和矿床地球化学的研究工作。E-mail: huruizhong@vip.gyig.ac.cn；周永章(1963—),男,教授,主要从事地球化学、大数据与数学地球科学等方面的研究工作。E-mail: zhouyz@mail.sysu.edu.cn
  • 作者简介:曹胜桃(1996—),男,博士研究生,地球化学专业。E-mail: 719733205@qq.com
  • 基金资助:
    国家自然科学基金项目(41830432);国家自然科学基金项目(U1812402);国家重点基础研究发展计划“973”项目“华南大规模低温成矿作用(2014CB440900);国家重点研发计划项目(2022YFF0801201);贵州省卡林型金矿成矿与找矿科技创新人才团队建设项目(黔科合平台人才-CXTD[2021]007)

Element enrichment pattern and prospecting method for Carlin-type gold deposits based on big data association rule algorithm

CAO Shengtao1,2(), HU Ruizhong1,2,*(), ZHOU Yongzhang3,*(), LIU Jianzhong4,5, TAN Qinping1, GAO Wei1, ZHENG Lulin5, ZHENG Lujing5, SONG Weifang5   

  1. 1. State Key Laboratory of Ore Deposit Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, China
    2. College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
    3. Center for Earth Environment & Resources, Sun Yat-sen University, Guangzhou 510275, China
    4. Bureau of Geology and Mineral Exploration and Development, Guiyang 550004, China
    5. Guizhou University, Guiyang 550025, China
  • Received:2024-02-26 Revised:2024-04-29 Online:2024-07-25 Published:2024-07-10

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摘要：

大数据时代的到来,为卡林型金矿床开拓了新的找矿思路。本研究应用关联规则算法,挖掘滇黔桂“金三角”卡林型金矿床内微量元素与金矿化海量数据之间的关联性,提取元素异常组合,分析控制因素,定量构建找矿标志。结果显示矿床内元素异常组合分为4组:(1)强正关联显著富集元素(As、Sb、Hg、Tl、Ag、W和Rb),显示硫化和黏土化作用;(2)较强正关联略富集元素I(Zr、Th、Ta、Nb和Hf)和强负关联强迁出元素(Li和Sr),显示去碳酸盐化作用;(3)较强正关联略富集元素II(Sn、Zn、Ni、V、Co和Cu),显示硫化作用;(4)弱关联基本无富集元素(Cd、Pb、Ba、Bi、U和Mo),与成矿无显著关联。从大数据角度获取的元素异常组合,与学界关于Au主要在去碳酸盐化、硫化和黏土化条件下形成的认识一致。通过关联规则算法分别对与硫化和去碳酸盐化相关的元素建立定量找矿标志。硫化找矿标志:样品中As、Hg、Sb、Tl、W、Ag和Rb等元素内中高含量项数≥1、≥2、≥3、≥4和≥5时,对应的Au矿化分别为≥4.5×10-9、≥47.0×10-9、≥150×10-9、≥500×10-9和≥1 000×10-9;样品内高含量项数≥1、≥2和≥3时,对应的Au矿化分别为≥150×10-9、≥500×10-9和≥1 000×10-9;找矿过程中两组指标配合使用,确保不漏矿,高效圈矿。去碳酸盐化找矿标志:样品中Zr、Th、Ta、Nb和Hf含量任意两项出现正异常,认为样品经历过去碳酸盐化作用。定量识别的硫化和去碳酸盐化找矿标志可望在卡林型金矿找矿预测中发挥重要作用。本研究基于关联规则算法分析矿床元素富集规律、控制因素和定量构建找矿标志的方法,也可为其他类型矿床开展类似研究提供新思路。

关键词: 地质大数据, 关联规则, 卡林型金矿, 元素富集规律, 控制因素, 找矿标志

Abstract:

Carlin-type gold deposits are an important reservoir of gold. Due to the gradual depletion of shallow surface gold resources, there is an urgent need for new prospecting methods to explore deep and hidden areas. The advent of the big data era has opened up new prospecting ideas. Association rule algorithm one of the most commonly used mining algorithms and can be used to effectively mine the inherent correlation between data items in large data sets. In this study, association rule mining is used to analyze the correlation between trace elements and gold mineralization in major Carlin-type gold deposits in the Yunnan-Guizhou-Guangxi “Golden Triangle” region. Combined with element migration and enrichment patterns, elemental anomaly combinations are extracted, and quantitative prospecting indicators are established. The elemental anomaly combinations are divided into elements with strong positive correlation and significantly enriched (As, Sb, Hg, Tl, Ag, W, Rb), indicating sulfidation and clayification (Rb); elements with strong positive correlation and slightly enriched (Zr, Th, Ta, Nb, Hf) or with strong negative correlation and strongly depleted (Li, Sr), indicating decarbonation; elements with strong positive correlation and slightly enriched (Sn, Zn, Ni, V, Co, Cu), likely reflecting their low contents in ore-forming fluids; and elements with weak correlation and not enriched (Cd, Pb, Ba, Bi, U, Mo)—these elements show no significant correlation with gold mineralization. The elemental anomaly combinations obtained by big data approach is consistent with previous understanding of the genesis of Au deposits, i.e., Au is mainly formed under decarbonation and sulfidation processes accompanied by significant clayification, in which sulfidation is the main genetic mechanism. Through association rule mining, quantitative prospecting indicators are established: For sulfidation related elements (As, Hg, Sb, Tl, W, Ag, Rb), when the number of medium-high content elements in samples ≥1, 2, 3, 4, or 5, the corresponding Au contents ≥4.5×10-9, 47.0×10-9, 150×10-9, 500×10-9, or 1000×10-9; when the number of high-content elements ≥1, 2, or 3, the corresponding Au contents ≥150×10-9, 500×10-9, or 1000×10-9; during prospecting, both indicators should be used to ensure efficient delineation of ore bodies, without outcrops. For decarbonation related elements (Zr, Th, Ta, Nb, Hf), decarbonation is indicated when elemental content anomaly occurs at any two of the elements in samples. The method developed in this study for establishing quantitative prospecting indicators based on association rule algorithms should provide new ideas for other types of mineral deposits.

Key words: geological big data, association rule algorithm, Carlin-type gold deposit, element enrichment law, factors of control, prospecting indicators

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