| 基于地质大数据理念的模型驱动矿产资源定量预测 |
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| 引用本文： | 于萍萍,陈建平,柴福山,郑 啸,于 淼,徐 彬.基于地质大数据理念的模型驱动矿产资源定量预测[J].地质通报,2015,34(7):1333-1343. |
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| 作者姓名： | 于萍萍 陈建平 柴福山 郑 啸 于 淼 徐 彬 |
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| 作者单位： | 1.中国地质大学（北京），北京 100083；2.北京市国土资源信息研究开发重点实验室，北京 100083；3.中国地质调查局发展研究中心，北京 100037；4.中国科学院遥感与数字地球研究所，北京 100094 |
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| 基金项目： | 中国地质调查局项目(编号：12120113091100)、国家自然科学基金项目（批准号：201011002）、科技部973计划项目（编号：2012CB416605） |
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| 摘 要： | 在大数据科学成为新的科学范式的背景下,基于地质大数据理念,提出了模型驱动的矿产资源定量预测评价的新方法,以及模型流程建模技术贯穿整个矿产资源预测评价过程的新思路,以地质理论指导地质大数据分析和计算机技术实现地质大数据挖掘2条主线展开研究,实现了面向地质大数据的数据挖掘与矿产资源的定量预测评价。结合青海祁漫塔格铁铜多金属矿床、山东焦家金矿床、云南个旧锡铜多金属矿床等不同地区、不同成矿类型和矿种开展了应用研究,完成了找矿模型工作流的设计与实现,进行了有利成矿信息的挖掘,取得了较好的效果,为大数据时代数字地质研究提供了新的思路。
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| 关 键 词： | 地质大数据 模型驱动 工作流 矿产资源定量预测 数据挖掘 数字地质 |
Research on model-driven quantitative prediction and evaluation of mineral resources based on geological big data concept |
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| YU Pingping,CHEN Jianping,CHAI Fushan,ZHENG Xiao,YU Miao,XU Bin.Research on model-driven quantitative prediction and evaluation of mineral resources based on geological big data concept[J].Geologcal Bulletin OF China,2015,34(7):1333-1343. |
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| Authors: | YU Pingping CHEN Jianping CHAI Fushan ZHENG Xiao YU Miao XU Bin |
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| Institution: | 1. 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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| Abstract: | 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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| Keywords: | geological big data model-driven workflow quantitative prediction of mineral resources data mining digital geology |
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