印度洋中脊多金属硫化物矿产资源定量预测与评价

邵珂, 陈建平, 任梦依. 印度洋中脊多金属硫化物矿产资源定量预测与评价[J]. 海洋地质与第四纪地质, 2015, 35(5): 125-133. doi: 10.16562/j.cnki.0256-1492.2015.05.015
引用本文: 邵珂, 陈建平, 任梦依. 印度洋中脊多金属硫化物矿产资源定量预测与评价[J]. 海洋地质与第四纪地质, 2015, 35(5): 125-133. doi: 10.16562/j.cnki.0256-1492.2015.05.015
SHAO Ke, CHEN Jianping, REN Mengyi. QUANTITATIVE PREDICTION AND EVALUATION OF POLYMETALLIC SULFIDE MINERAL DEPOSITS ALONG THE CENTRAL INDIAN OCEAN RIDGE[J]. Marine Geology & Quaternary Geology, 2015, 35(5): 125-133. doi: 10.16562/j.cnki.0256-1492.2015.05.015
Citation: SHAO Ke, CHEN Jianping, REN Mengyi. QUANTITATIVE PREDICTION AND EVALUATION OF POLYMETALLIC SULFIDE MINERAL DEPOSITS ALONG THE CENTRAL INDIAN OCEAN RIDGE[J]. Marine Geology & Quaternary Geology, 2015, 35(5): 125-133. doi: 10.16562/j.cnki.0256-1492.2015.05.015

印度洋中脊多金属硫化物矿产资源定量预测与评价

  • 基金项目:

    中国大洋协会"十二五"重大项目(DY125-11-R-02)

详细信息
    作者简介: 邵珂(1991-),女,硕士生,地质工程专业,E-mail:914709527@qq.com
  • 中图分类号: P744.4

QUANTITATIVE PREDICTION AND EVALUATION OF POLYMETALLIC SULFIDE MINERAL DEPOSITS ALONG THE CENTRAL INDIAN OCEAN RIDGE

  • 大洋钻探资料证实,现代海底多金属硫化物分布范围广泛、储量大,是具有巨大开发潜力和远景的海底矿产资源。根据水深、地质构造、扩张速率、地球物理以及火山地震等区域性调查数据,分析了印度洋中脊多金属硫化物成矿地质条件、控矿因素和地球物理异常信息,提取了9项找矿证据因子,建立了区域找矿有利条件组合模型。运用证据权重法,对印度洋中脊多金属硫化物资源进行了基于数据驱动的定量预测与评价。研究认为,最有利区(Ⅰ类)占工区面积的29.77%,比较有利区(Ⅱ类)占18.12%。
  • 加载中
  • [1]

    刘因,王金锋,刘辉.GIS成矿预测的数据驱动与知识驱动分析[J].安徽地质,2003,13(2):126-129.

    [LIU Yin, WANG Jinfeng, LIU Hui. Problem of process driven by data and knowledge in metallogenic prognosis[J].Geology of Anhui,2003,13(2):126-129.]

    [2]

    Hannington M, Jamieson J, Monecke T, et al. The abundance of seafloor massive sulfide deposits[J]. Geology, 2011, 39(12):1155-1158.

    [3]

    Small C. Global systematics of mid-ocean ridge morphology[C]//Faulting and Magmatism at Mid-Ocean Ridges. Buck W R, Delaney P T, Karson J A, et al. AGU Geophysical Monograph, 1998:106:1-26.

    [4]

    Carbotte S,Scheirer D S. Variability of Ocean Crustal Structure Created along the Global Mid-ocean Ridge[M]. Hydrogeology of the Oceanic Lithosphere. Davis E E, Elderfield H, Cambridge University Press, 2004:59-107.

    [5]

    Mendel V, Sauter D, Parson L, et al. Segmentation and morphotectonic variations along a super slow-spreading center:The Southwest Indian Ridge (57°E-70°E)[J].Marine Geophysical Researches, 1997, 19(6):505-533.

    [6]

    Fouquet Y. Where are the large hydrothermal sulphide deposits in the oceans?[M].Philosophical Transactions:Mathematical, Physical and Engineering Sciences. The Royal Society, 1997, 355:427-441.

    [7]

    高爱国. 海底热液活动研究综述[J].海洋地质与第四纪地质,1996, 16(1):103-110.

    [GAO Aiguo. Summarizing on the study of hydrothermal activities on the seafloor[J].Marine Geology and Quaternary Geology,1996, 16(1):103-110.]

    [8]

    别风雷,李胜荣,侯增谦,等. 现代海底多金属硫化物矿床[J]. 成都理工学院学报,2000,27(4):335-342.

    [BIE Fenglei, LI Shengrong, HOU Zengqian, et al. Polymentallic sulfide deposits at modern seafloor:an overview[J]. Journal of Chengdu University of Technology, 2000, 27(4):335-342.]

    [9]

    German C R, et al. Hydrothermal activity along the southwest Indian ridge[J]. Nature, 1998, 395(6701):490-493.

    [10]

    Agterberg F P, Bonham Charter G F, Cheng Q, et al. Weights of evidencemo delingand weighted logistic regression formineral potential mapping:Computers in Geology[M]. Cambridge:Oxford University Press, 1993:13-32.

    [11]

    Agterherg F P. Combining indicator patterns in weights of evidence modeling for resource evaluation[J]. Nonrenewable Resources, 1992, 1(1):39-50.

    [12]

    夏建新,李畅,马彦芳.深海底热液活动研究热点[J].地质力学学报,2007,13(2):179-191.

    [XIA Jianxin,LI Chang, MA Yanfang. Deep-sea hydrothermal activity:a hot research topic[J].Journal of Geomechanics,2007,13(2):179-191.]

    [13]

    赵鹏大.矿床统计预测[M].北京:地质出版社,1994.[ZHAO Pengda. Statistical Prediction of Mineraldeposit[M].Beijing:Geological Press,1994.]

    [14]

    景春雷.海底热液多金属硫化物成矿区域地质背景与控矿因素分析[D].青岛:国家海洋局第一海洋研究所,2012.[JING Chunlei. Analysis on the Regional Geological Background and Ore-controlling Factors of Submarine Hydrothermal Sulfide[D].Qingdao:The first Institute of Oceanography, SOA,2012.]

    [15]

    孟祥君,张训华,刘怀山,等.中国东部海区磁异常特征及其地质解释[J].海洋地质与第四纪地质,2014, 34(3):67-74.

    [MENG Xiangjun, ZHANG Xunhua, LIU Huaishan, et al. The magnetic anomaly pattern in The Eastern China Seas and its geological interpretation[J].Marine Geology and Quaternary Geology, 2014, 34(3):67-74.]

    [16]

    Mendel V, Sauter D. Seamount volcanism at the super slow-spreading Southwest Indian Ridge between 57°E and 70°E[J].Geology,1997,25:99-102.

    [17]

    陈永清,汪新庆,陈建国,等.基于GIS的矿产资源综合定量评价[J].地质通报,2007,26(2):141-149.

    [CHEN Yongqing,WANG Xinqing, CHEN Jianguo, et al. GIS-based integrated quantitative assessments of mineral resources[J].Geological Bulletin of China, 2007,26(2):141-149.]

    [18]

    刘世翔.薛林福,郄瑞卿,等.基于GIS的证据权重法在黑龙江省西北部金矿成矿预测中的应用[J].吉林大学学报:地球科学版,2007,37(5):889-894.

    [LIU Shixiang, XUE Linfu, QIE Ruiqing,et al. An application of GIS-Based weights of evidence for gold prospecting in the Northwest of Heilongjiang Province[J].Journal of Jilin Univerisity(Earth Science Edition), 2007,37(5):889-894.]

    [19]

    刘星,胡光道.应用MORPAS系统证据权重法进行多源信息成矿预测——以澜沧江南段地区为例[J].地质与勘探,2003,39(4):65-68.

    [LIU Xing,HU Guangdao. Applying evidence weight model of MORPAS system to process multi-source information for predicting minerals resources-example from the southern Lancangjiang area[J]. Geology and Prospecting, 2003,39(4):65-68.]

    [20]

    李健,丁成武,陈建平,等.基于GIS的证据权重法的甘肃北祁连东段海相火山岩型铅锌矿床成矿预测评价[J]. 兰州大学学报:自然科学版,2012,48(4):14-19.

    [LI Jian,DING Chengwu,CHEN Jiangpin,et al. Metallogenic prognosis of marine volcanic type lead-zinc resources in metallogenic belt of northern Qilian based on GIS weight of evidence[J]. Journal of Lanzhou University(Natural Sciences), 2012,48(4):14-19.]

  • 加载中
计量
  • 文章访问数:  1308
  • PDF下载数:  7
  • 施引文献:  0
出版历程
收稿日期:  2014-10-13
修回日期:  2014-12-24

目录