| 基于空间位置的矿产资源关联分析和预测方法:以我国西部地区为例 |
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| 引用本文： | 刘国,王懿哲,薛涛,吴晨垚,薛博,唐甜甜,刘士铭.基于空间位置的矿产资源关联分析和预测方法:以我国西部地区为例[J].现代地质,2019,33(4):751-758. |
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| 作者姓名： | 刘国 王懿哲 薛涛 吴晨垚 薛博 唐甜甜 刘士铭 |
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| 作者单位： | 1.国家地理信息系统工程技术研究中心,湖北 武汉 4300742.中国地质图书馆,北京 1000833.自然资源部国土卫星遥感应用中心,北京 1000484.中国地质大学(北京) 信息工程学院,北京 100083 |
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| 基金项目： | 中国地质调查局项目“地学文献数据采集整合与服务”(121201015000150003) |
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| 摘 要： | 基于矿产资源空间位置的研究方法对勘探找矿具有重要意义。设计开发了基于空间位置关联分析和预测方法,该方法根据隐伏矿体预测的相似-类比理论,通过分析已公开的矿产资源空间分布数据,获取已知矿种的相关性,进一步对隐伏矿体进行分析和预测。以我国西部部分地区的矿产资源空间分布数据为样本,采用Apriori算法分析已知矿种空间位置上的关联关系,通过分析得出该区域矿种的共生和伴生等关联关系,获得隐伏矿体的预测结果。通过与现有成果对比证明该方法的有效性和可行性,利用GoogleEarth等可视化的工具进行预测结果展现,以便更好地进行对比研究。研究的创新点在于设计开发了基于空间位置关联分析和预测方法,利用该方法对已知矿种空间位置进行关联分析,推导出所在区域矿种的共生和伴生等关系,对隐伏矿体进行了分析预测。
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| 关 键 词： | 矿产预测 网络爬虫 关联分析 Apriori算法 |
| 收稿时间： | 2018-01-22 |
| 修稿时间： | 2019-02-20 |
Mineral Resource Spatial Association Analysis and Prediction:A Case Study in Western China |
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| LIU Guo,WANG Yizhe,XUE Tao,WU Chenyao,XUE Bo,TANG Tiantian,LIU Shiming.Mineral Resource Spatial Association Analysis and Prediction:A Case Study in Western China[J].Geoscience——Journal of Graduate School,China University of Geosciences,2019,33(4):751-758. |
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| Authors: | LIU Guo WANG Yizhe XUE Tao WU Chenyao XUE Bo TANG Tiantian LIU Shiming |
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| Institution: | 1. National Engineering Research Center for Geographic Information System, Wuhan,Hubei 430074,China2. National Geological Library of China,Beijing 100083,China3. Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China4. Laboratory of Software Engineering, China University of Geosciences,Beijing 100083, China |
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| Abstract: | Based on the similarity-analogy theory of concealed orebody prediction, a method of spatial location correlation analysis and prediction is designed and developed. By analyzing the published spatial distribution data of mineral resources, correlation of known mineral occurrences is performed to analyze and predict the concealed orebodies. Taking some areas of Western China as an example, the Apriori algorithm is used to analyze the relationship between the spatial location of known mineral occurrences, and the relationship between the symbiosis and mineral association in that area. Consequently, prediction is made on the locations of concealed orebodies. Effectiveness and feasibility of this method are proven by comparing with the existing results. Visualization tools, such as GoogleEarth, are used to show the predicted results, and to make a better comparative study. This project has newly designed and developed a method based on spatial location correlation analysis and prediction. |
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| Keywords: | mineral prediction web crawl association analysis Apriori algorithm |
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