| 基于GOCAD软件的沙子江铀矿床三维定量预测 |
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| 引用本文： | 耿瑞瑞,范洪海,孙远强,夏宗强,孙雨鑫,俞嘉嘉,陈东欢.基于GOCAD软件的沙子江铀矿床三维定量预测[J].矿床地质,2020,39(6):1078-1090. |
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| 作者姓名： | 耿瑞瑞 范洪海 孙远强 夏宗强 孙雨鑫 俞嘉嘉 陈东欢 |
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| 作者单位： | 核工业北京地质研究院,北京 100029;东华理工大学,江西南昌 344001 |
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| 基金项目： | 本文得到中核集团集中研发项目“南方重点花岗岩型铀矿基地资源预测与扩大”（编号：LTD1602）和中核集团产研结合研究项目“华南富大铀矿空间定位条件与远景预测研究”（编号：202035-6）联合资助 |
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| 摘 要： | 文章采用GOCAD软件构建了沙子江铀矿床的三维地质模型，包括岩体模型、构造模型以及矿体模型等，利用其强大的空间分析模块进行地质体、构造、不同岩性接触界面、地球物理场等三维空间属性的统计分析及成矿信息提取，并通过序贯高斯模拟的插值方法建立了沙子江铀矿床矿体品位的三维属性模型，进而揭示铀矿化的空间分布规律。通过对成矿地质条件、控矿要素以及地球物理异常特征识别标志等铀成矿有利信息的提取，建立了沙子江铀矿床的定量预测模型。基于三维信息量法成矿预测方法，计算各预测要素的成矿有利区间，圈定7片找矿有利靶区，为将来的钻探工程部署及深部铀矿找矿突破提供依据。
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| 关 键 词： | 地质学 GOCAD软件地质学 多元铀成矿信息提取 三维地质建模 铀成矿定量预测 沙子江铀矿床 |
| 收稿时间： | 2020/3/20 0:00:00 |
| 修稿时间： | 2020/11/16 0:00:00 |
3D quantitative prediction of Shazijiang uranium deposit based on GOCAD software |
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| GENG RuiRui,FAN HongHai,SUN YuanQiang,XIA ZongQiang,SUN YuXin,YU JiaJi,CHEN DongHuan.3D quantitative prediction of Shazijiang uranium deposit based on GOCAD software[J].Mineral Deposits,2020,39(6):1078-1090. |
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| Authors: | GENG RuiRui FAN HongHai SUN YuanQiang XIA ZongQiang SUN YuXin YU JiaJi CHEN DongHuan |
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| Institution: | Beijing Research Institute of Uranium Geology, Beijing 100029, China;East China University of Technology, Nanchang 344001, Jiangxi, China |
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| Abstract: | GOCAD software was used to construct a three-dimensional geological model of the Shazijiang uranium deposit in this paper, including rock mass model, tectonic model and orebody model. Based on spatial statistical analysis of geological bodies, structures, contact interfaces and geophysical fields, the authors obtained significant mineralization information for the uranium deposit, and then further established a three-dimensional property model of uranium grade by the interpolation method of sequential Gaussian simulation so as to reveal their spatial distribution regularity. Combined with various kinds of information related to uranium mineralization, such as metallogenic geological conditions, ore-controlling factors and geophysical anomaly identification, the authors established a quantitative prediction model for the Shazijiang uranium deposit. In addition, seven potential targets were predicted in this study by assessment of each ore-forming condition using the three-dimensional prospecting information method, which would provide a guideline for future drilling project and prospecting breakthrough of deep uranium orebodies in this region. |
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| Keywords: | geology GOCAD software mineralization information extraction 3D geological modeling quantitative prediction Shazijiang uranium deposit |
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