地学前缘 ›› 2019, Vol. 26 ›› Issue (4): 131-137.DOI: 10.13745/j.esf.2019.04.015

• 大数据算法研究 • 上一篇    下一篇

推荐系统算法在钦杭成矿带南段文地幅矿床预测中的应用

王堃屹，周永章，王俊，张奥多，余晓彤，焦守涛，刘心怡   

  1. 1. 中山大学 地球科学与工程学院, 广东 广州 510275
    2. 中山大学 地球环境与地球资源研究中心, 广东 广州 510275
    3. 广东省地质过程与矿产资源探查重点实验室, 广东 广州 510275
    4. 广东高质资源环境研究院, 广东 广州 510275
  • 收稿日期:2019-04-30 修回日期:2019-05-17 出版日期:2019-07-25 发布日期:2019-07-25
  • 通讯作者: 周永章(1963—)，男，教授，博士生导师，主要从事大数据、数学地质与地球化学研究。
  • 作者简介:王堃屹(1995—)，男，硕士研究生，地球化学专业。
  • 基金资助:
    国家重点研发计划项目(2016YFC0600506)；国家自然科学基金项目(41273040)；中国地质调查局项目(12120113067600)

Recommendation system algorithm and its application in ore deposits forecast at Wendi district of the southern Qinzhou-Hangzhou metallogenic belt, South China

WANG Kunyi,ZHOU Yongzhang,WANG Jun,ZHANG Aoduo, YU Xiaotong,JIAO Shoutao,LIU Xinyi   

  1. 1. School of Earth Sciences & Engineering, Sun Yat-sen University, Guangzhou 510275, China
    2. Center for Earth Environment & Resources, Sun Yat-sen University, Guangzhou 510275, China
    3. Guangdong Provincial Key Laboratory of Mineral Resources and Geological Processes, Guangzhou 510275, China
    4. Guangdong Gaozhi Institute of Resources and Environment, Guangzhou 510275, China
  • Received:2019-04-30 Revised:2019-05-17 Online:2019-07-25 Published:2019-07-25
  • Supported by:
     

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摘要： 推荐系统算法是大数据挖掘的重要技术之一。根据钦杭成矿带南段文地幅1∶5万地质矿产调查所获得的数据，研究选取中型银金矿床、小型银金矿床(点)、确证无银金矿床、未评价区域(针对银金矿床)、中型铅锌矿床、小型铅锌矿床(点)、确证无铅锌矿床、未评价区域(针对铅锌矿床)作为待预测的能动项，选取加里东期混合岩、燕山早期侵入岩、燕山晚期侵入岩、北东向断裂、北西向断裂以及Au、Ag、Pb、Zn元素作为因素项，运用基于内容的推荐系统算法，构建能动项因素项的效用矩阵，计算已知矿床(点)与其他未评价区域之间的相似度，进而预测银金矿床和铅锌矿床潜在的找矿区域。实验结果表明，推荐系统算法能够较好地挖掘与成矿有关的信息，快速抓取出与某类矿床(点)相似的潜在成矿区域。对于银金矿床，相似度较高的区域主要分布在已知矿点周围以及北东向断裂的两侧，少量分布于叠加断裂附近。对于铅锌矿床，中型铅锌矿床的结果显示出较高的区分度，高值区基本涵盖了所有已知的铅锌矿点，小型铅锌矿床的结果更加集中。除已知矿点外，还有几处高值区可作为重点的找矿靶区。

 

关键词: 大数据挖掘, 推荐系统算法, 效用矩阵, 矿床预测, 钦杭成矿带, 广西文地幅

Abstract: Recommendation system algorithm is one of the important technologies of big data mining. In this study, we applied the content-based recommendation system algorithm to construct an utility matrix of the active and factor items, based on data obtained from the 1∶50000 geological and mineral resource survey at the Wendi district of southern Qinzhou-Hangzhou metallogenic belt, South China. The predicted active item included the middle- and small-sized (ore spot) electrum and verified non-electrum deposits; it also included the unevaluated electrum deposits, medium- and small-sized (ore spot) lead-zinc and verified non-lead-zinc (ore spot) deposits, and the unevaluated lead-zinc deposits. Caledonian migmatite, early Yanshanian and late Yanshanian intrusions, NE- and NW-trending faults, and Au, Ag, Pb and Zn elements were considered the factor item. The Euclidean distance similarity between known deposit (or ore spot) and other unevaluated areas was calculated and then used to predict the prospecting area of silver-gold and lead-zinc deposit. The results show that the recommendation system algorithm can effectively mine mineralization related information, and quickly extract the potential deposit areas based on its similarity to certain types of deposits (ore spot). For electrum deposit within the Wendi district, high similarity areas were mainly distribute around known ore deposits and on both sides of NE-trending faults, with a small portion distributed near the overlapped fracture. In comparison, for lead-zinc deposits, medium-sized deposit showed a high degree of discrimination. The high-value areas covered almost all known lead-zinc deposits while more concentrated distribution were found for small-sized deposits. In addition to known deposits, several high value areas can also be used as key prospecting targets.

Key words: big data mining, recommendation system algorithm, utility matrix, ore deposit forecast, Qinzhou-Hangzhou metallogenic belt, Guangxi Wendi district

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