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XIAO Keyan, LI Nan, WANG Kun, SUN Li, FAN Jianfu, DING Jianhua. 2015: Mineral resources assessment under the thought of big data. Geological Bulletin of China, 34(7): 1266-1272.
Citation: XIAO Keyan, LI Nan, WANG Kun, SUN Li, FAN Jianfu, DING Jianhua. 2015: Mineral resources assessment under the thought of big data. Geological Bulletin of China, 34(7): 1266-1272.
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Mineral resources assessment under the thought of big data

  • Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing, China;2. School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing, China

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  • In this paper, the basic theoretical foundation of mineral resources prediction and evaluation is explored, with the prediction thinking method in the age of Big Data and the work of the mineral resources potential assessment in details. It is considered that the prediction method of the big data is relativity consistent with the common comprehensive information mineral prediction method. The four fundamental theories include mineral prediction model theory, multidisciplinary information correlation analysis, geological theory of dissimilation and trend analysis of mineral area. The main workflow of mineral resources assessment in the information age and digital era is summarized. The basic tasks and processes are building the data platform for digital prediction, data cleaning according to mineral prediction model, preparing the forecast figure, building prediction model, delineating metallogenic target area and minerogenic prospect, and estimating the potential resources.
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