| 铜陵矿集区姚家岭锌金多金属矿床深部地质空间信息相关性数据挖掘 |
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| 引用本文： | 周国玉,张明明,沈乐,张淑虹,袁峰,李晓晖,季斌,周宇章.铜陵矿集区姚家岭锌金多金属矿床深部地质空间信息相关性数据挖掘[J].大地构造与成矿学,2020(2):242-250. |
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| 作者姓名： | 周国玉 张明明 沈乐 张淑虹 袁峰 李晓晖 季斌 周宇章 |
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| 作者单位： | 合肥工业大学资源与环境工程学院;合肥工业大学安徽省矿产资源与矿山环境工程技术研究中心;合肥工业大学空间信息集成与综合分析平台;安徽省地质测绘技术院;广东省地质局第六地质大队;安徽省公益性地质调查管理中心 |
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| 基金项目： | 国家自然科学基金项目(41872247、41402287、41702353);国家重点研发计划项目(2016YFC0600209);国家自然科学基金重点国际合作研究项目(41320104003);中国科学院“西部之光”人才培养引进计划项目;安徽省国土资源科技项目(2016-K-4)联合资助。 |
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| 摘 要： | 姚家岭锌金多金属矿床是长江中下游成矿带铜陵矿集区近年来新发现的特大型热液多金属矿床,矿床位于铜陵断隆区与繁昌断凹区的过渡部位,成矿过程受构造、裂隙和矿液运移模式等因素的控制。成矿作用分为多个阶段,矿床范围内蚀变作用强烈,蚀变类型复杂多样,矿化不均匀。当前大数据思维为地质研究开辟了新思路,采用全数据模式、从数据出发的大数据分析方法可以有效探索研究矿床。基于姚家岭矿床的钻孔数据,结合已有研究成果,创建深部数据挖掘范围,在该范围内采用反距离权重插值法建立姚家岭块体模型,然后选择三维欧式距离场及空间相关程度定量化分析对深部空间信息进行相关性数据挖掘。结果表明,姚家岭矿床的铅锌矿体、金矿体和铜矿体与二叠系栖霞组重叠超过50%,60%左右铅锌矿体与石炭系的空间距离在500 m以内,80%左右金矿体和铜矿体与石炭系的空间距离在500 m以内。铅锌矿体与角砾斑岩和角砾大理岩的空间相关性最高,相关程度分别为40.37%和24.77%;金矿体与角砾斑岩的空间相关性最高,相关程度分别为13.76%和5.5%;铜矿体与角砾斑岩、角砾大理岩和角砾灰岩的相关性最高,相关程度分别为36.17%、16.51%和15.60%。
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| 关 键 词： | 大数据思维 数据挖掘 定量化分析 姚家岭 锌金多金属矿床 |
Data Mining of Deep Geological Spatial Information of the Yaojialing Zinc-gold Polymetallic Deposit |
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| ZHOU Guoyu,ZHANG Mingming,SHEN Le,ZHANG Shuhong,YUAN Feng,LI Xiaohui,JI Bin,ZHOU Yuzhang.Data Mining of Deep Geological Spatial Information of the Yaojialing Zinc-gold Polymetallic Deposit[J].Geotectonica et Metallogenia,2020(2):242-250. |
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| Authors: | ZHOU Guoyu ZHANG Mingming SHEN Le ZHANG Shuhong YUAN Feng LI Xiaohui JI Bin ZHOU Yuzhang |
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| Institution: | (School of Resources and Environmental Engineering,Hefei University of Technology,Hefei 230009,Anhui,China;Anhui Province Engineering Research Center for Mineral Resources and Mine Environments,Hefei University of Technology,Hefei 230009,Anhui,China;Spatial Information Integration and Integrated Analysis Platform,Hefei University of Technology,Hefei 230009,Anhui,China;Geological Surveying and Mapping Technology of Anhui Province,Hefei 230022,Anhui,China;Guangdong Geological Bureau Sixth Geological Brigade,Jiangmen 529000,Guangdong,China;Anhui Public Geological Survey Management Center,Hefei 230008,Anhui,China) |
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| Abstract: | The Yaojialing Zn-Au-polymetallic deposit,located in the transitional area between the Tongling uplift and the Fanchang fault basin,is a recently discovered large zinc-gold deposit in the middle and lower Yangtze River.The ore-forming process is controlled by structure,fracture and ore fluid migration mode.The mineralization can be divided into several stages.Within the orefield,the alteration is strong and complicated,and the mineralization is unevenly distributed.Being a new geological approach,big data analysis is based on full data model,and therefore,can effectively explore and study a deposit.This paper establishes the deep data mining range based on the drilling data and existing research results of a deposit.In this study,the Yaojialing block model is constructed using the inverse distance weighting method.The block model then is used in calculation of the three-dimensional Euclidean distance field and quantitative analysis of spatial correlation degree to dig the correlation data of the raw data.The results showed that more than 50%of the Pb-Zn ore bodies,gold ore bodies,and copper ore bodies of the Yaojialing deposit are spatially overlapping with the Permian Qixia Formation,and the distance between the Pb-Zn ore bodies and the Carboniferous strata is within 500 m,and 60%of the ore bodies are spatially overlapping with the Carboniferous sediments.The gold ore bodies and the copper ore bodies also occur closely within the Carboniferous strata,with a distance of no more than 500 m,and more than 80%of the ore bodies are spatially overlapping with the Carboniferous strata.The values of spatial correlation between lead-zinc ore bodies and breccia porphyry and breccia marble are the highest,with the correlation degree of 40.37%and 24.77%,respectively.The spatial correlation between gold ore bodies and breccia porphyry are the highest,with correlation degree of 13.76%and 5.5%,respectively.The copper ore body is closely correlated with breccia porphyry,breccia marble and breccia limestone,and the correlation degree is 36.17%,16.51%and 15.60%,respectively. |
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| Keywords: | big data thinking data mining quantitative analysis Yaojialing Zn-Au-polymetallic deposit |
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