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    Volume 26 Issue 2
    Mar.  2001
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    Article Contents
    CHEN Yong-qing, LIU Hong-guang, 2001. A PRELIMINARY VIEW ON DIGITAL PATTERN FOR MINERAL EXPLORATION BASED ON GEOANOMALY. Earth Science, 26(2): 129-134.
    Citation: CHEN Yong-qing, LIU Hong-guang, 2001. A PRELIMINARY VIEW ON DIGITAL PATTERN FOR MINERAL EXPLORATION BASED ON GEOANOMALY. Earth Science, 26(2): 129-134.

    A PRELIMINARY VIEW ON DIGITAL PATTERN FOR MINERAL EXPLORATION BASED ON GEOANOMALY

    • Received Date: 2000-12-29
    • Publish Date: 2001-03-25
    • Digital pattern for mineral exploration is a powerful tool for digital mineral exploration projects. Preliminarily this paper expounds (a) the fundamental theory of the digital pattern for mineral exploration; (b) the features of geological geochemical geophysical and remote sensing information and their functions for mineral exploration; (c) the procedure for extraction connection transformation and integration of ore-finding information and (d) the methods of establishment of digital pattern for mineral exploration based on geoanomaly. In fact the process of extraction and synthesis of ore-finding information is also a process of establishing a digital pattern for mineral exploration.

       

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    • [1]
      赵鹏大陈永清. 地质异常矿体定位的基本途径[J]. 地球科学———中国地质大学学报1998 23(2): 111~114. https://www.cnki.com.cn/Article/CJFDTOTAL-DQKX802.000.htm
      [2]
      赵鹏大陈永清刘吉平等. 地质异常成矿预测理论与实践[M]. 武汉: 中国地质大学出版社1999.138.
      [3]
      赵鹏大, 陈永清, 金友渔. 基于地质异常的"5P"找矿地段的定量圈定与评价[J]. 地质论评2000, 46(增刊): 1~12. https://www.cnki.com.cn/Article/CJFDTOTAL-DZLP2000S1005.htm
      [4]
      Gorelov D A. Quantitative characteristics of geological anomalies in assessing ore capacity[J]. Internal Geology Review, 1982 (4): 457~465.
      [5]
      Laznicak P. Giant ore deposits: a quantitative approach[J]. Global Tectonics Metallogeny, 1983 2(1&2): 41~63.
      [6]
      Laznicak P. Quantitative relationships among giant deposits of metals[J]. Economic Geology, 1999, 94(4): 455~473. doi: 10.2113/gsecongeo.94.4.455
      [7]
      Schuiling R D. 环大西洋大陆上的锡矿带[A]. 见: 赖特J B, 主编. 矿床、大陆漂移和板块构造[C]. 陈昌明, 陈志明, 译. 北京: 地质出版社1982.17~22.
      [8]
      谢学锦向运川. 巨型矿床的预测方法[A]. 见: 谢学锦, 邵跃, 王学求, 主编. 走向21世纪矿产勘查地球化学[C]. 北京: 地质出版社1999.61~91.
      [9]
      陈永清, 赵鹏大, 刘红光. 鲁西金矿成矿组分的聚集与演化[J]. 地球科学———中国地质大学学报, 2001, 25(1): 51~58. https://www.cnki.com.cn/Article/CJFDTOTAL-DQKX200101008.htm
      [10]
      Chen Y Q, Liu H G. Delineation of potential mineral resources region based on geoanomaly unit[J]. Journal of China University of Geosciences, 2000, 11(2): 158~163.
      [11]
      Pan G C, Harris D P. Information synthesis for mineral exploration[M]. New York: Oxford University Press, 2000.461.
      [12]
      McCammon R B, Botbal J M, Larsen R S. Characteristic analysis-1981: final program and a possible discovery[J]. Math Geology, 1983, 15(1): 59~84. doi: 10.1007/BF01030076
      [13]
      Pan G C. Canonical favorability model for data integration and mineral potential mapping[J]. Comput Geosci, 1993, 19: 1077~1100. doi: 10.1016/0098-3004(93)90016-X
      [14]
      Agterberg F P, Bonham-Cater G F, Cheng Q, et al. Weights of evidence modeling and weighted logistic regression for mineral potential mapping[A]. In: Davis J C, Herzfield U C, eds. Computer in Geology-25 Years of Progress[C]. New York: Oxford University Press, 1993.13~32.
      [15]
      Pan G C. Extended weights of evidence modeling for the pseudo-estimation of metal grades[J]. Nonrenew Resour, 1996, 5(1): 53~76. doi: 10.1007/BF02259070
      [16]
      Hu G D, Chen J G, Chen S Y. Metallic mineral resources assessment and analysis system design[J]. Journal of China University of Geosciences, 2000, 11(3): 308~311.
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