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YU Pingping, CHEN Jianping, CHAI Fushan, ZHENG Xiao, YU Miao, XU Bin. 2015: Research on model-driven quantitative prediction and evaluation of mineral resources based on geological big data concept. Geological Bulletin of China, 34(7): 1333-1343.
Citation: YU Pingping, CHEN Jianping, CHAI Fushan, ZHENG Xiao, YU Miao, XU Bin. 2015: Research on model-driven quantitative prediction and evaluation of mineral resources based on geological big data concept. Geological Bulletin of China, 34(7): 1333-1343.
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Research on model-driven quantitative prediction and evaluation of mineral resources based on geological big data concept

  • China University of Geosciences (Beijing), Beijing 100083, China;2. Beijing Key Laboratory of Development and Research for Land Resources Information, Beijing 100083, China;3. Development and Research Center of China Geological Survey, Beijing 100037, China;4. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China

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  • In the context of the situation that big data science has become a new scientific paradigm, this paper presented the new method of model-driven quantitative prediction and evaluation of mineral resources and the new idea of modeling techniques throughout the entire process of mineral resources prediction based on geological big data concept. This study truly realized the data mining and quantitative prediction and evaluation of mineral resources for the geological big data by two main lines of geological theory guiding geological big data analysis and computer technology achieving geological big data mining. The application study of this methodology was carried out in different regions, different types of mineralization and different minerals like the Qimantag iron and copper polymetallic deposits of Qinghai Province, the Jiaojia gold deposit of Shandong Province and the Gejiu tin-copper polymetallic deposit of Yunnan Province. The authors designed and implemented the prospecting model workflow, sufficiently analyzed and extracted favorable mineralization information and obtained good results which could provide new idea for digital geological studies and quantitative prediction and evaluation of mineral resources in the big data age.
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