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利用大数据挖掘矿石中主要矿物之间的关系
The application of big data to exploring the relationships between major minerals in ores
投稿时间:2020-06-11  修订日期:2020-07-21
中文关键词:矿石  矿物成分  关联规则  社团检测  大数据  数据挖掘
英文关键词:ores  mineral composition  association rules  community detection  big data  data mining
基金项目:国家重点研发计划"深地资源勘查开采"重点专项(2016YFC0600502)
作者单位E-mail
陈军林 中国地质大学(北京) 地球科学与资源学院, 北京 100083  
闫岩 中国地质大学(北京) 地球科学与资源学院, 北京 100083  
彭润民 中国地质大学(北京) 地球科学与资源学院, 北京 100083 cprm@cugb.edu.cn 
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中文摘要:
      近年来,借助于大数据技术的发展,地质学迎来了新的发展机遇,但目前利用大数据技术来分析矿物之间关系的研究还比较少。矿物是岩石、矿石的基本组成要素,通常都是以共存集合体的形式产出。矿物的产出不是随机的,而是按照一定的规律共生、伴生在一起。通过大数据技术挖掘出这种矿物的共伴生规律,能够更好地认识矿物之间的关系,对于指导找矿实践有积极作用。本文利用频繁模式挖掘、关联规则、网络分析以及社团检测这些常用的大数据挖掘方法进行了矿石主要组成矿物的大数据分析。所使用的矿石矿物组成数据来自于美国地质调查局的全球矿产资源数据系统(MRDS),该数据集收集了来自于全球的大量矿床中矿石的矿物组成数据。研究结果显示,通过关联规则可以挖掘出隐藏在矿石矿物成分大数据集中的频繁矿物组合,对于找矿勘查和认识矿物之间的关系有积极作用;关联规则挖掘出的规则是一种量化的推理规则,通过兴趣度度量指标能够定量地表征规则的强弱,这种规则相比于经验总结的规律更加定量化和精细化;通过网络分析能够对矿石中主要矿物之间的关系和共伴生规律进行动态、多维、定量的可视化;再结合社团检测可以从矿石矿物数据集中发现隐藏在其中的矿物之间的关系。
英文摘要:
      In recent years, with the development of big data, geology has met new opportunities for development. Nevertheless, there are still relatively insufficient studies that use big data to study the relationships between different minerals. The basic components of minerals, rocks and ores usually exist in the form of coexisting assemblages. The occurrence of minerals is not random, but coexists and accompanies with each other according to some certain pattern. Mining co-occurrence pattern of these minerals through big data technology and mining the relationship between minerals can help better understand the relationships between minerals and can also play a positive role in guiding mineral prospecting. In this study, the authors used association rules, frequent pattern mining, network analysis, and community detection, which are commonly-used big data mining methods, to analyze the big data of the main components of ores. The dataset used in this paper was from the "Mineral Resources Data System" (MRDS) of the U.S. Geological Survey, which has collected a large number of mineral composition data from all over the world. The results show the following features:① Frequent mineral assemblages concealed in ore mineral composition dataset can be discovered through association rule mining. The frequent mineral assemblages are useful in mineral prospecting and the understanding of the relationship between minerals; ② The rules mined by association rule mining are a kind of quantitative reasoning rules. The interest measurement index can quantitatively represent the strength of rules. These rules are more quantitative and refined than the rules summarized by experience; ③ By means of network analysis, the relationship between main minerals in the ore dataset can be visualized dynamically, multi-dimensionally and quantitatively. Combined with community detection, the hidden relationship between minerals can be found from the ore mineral data set.
陈军林,闫岩,彭润民,2020,利用大数据挖掘矿石中主要矿物之间的关系[J].岩石矿物学杂志,(5):605~614.
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