地质大数据方法在区域找矿靶区定量优选中的应用
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摘要
通过查明数字间的相关关系,研究、解决地质问题的大数据研究思想和方法,已被越来越多的地质工作者接受并应用。文中应用大数据思想、方法对甘肃省西秦岭地区1∶20万区域水系沉积物测量数据进行深度挖掘,构建起"基于化探信息的区域Au找矿靶区定量优选系列模型"。系列模型不但对研究区Au找矿靶区做出了精确的定量预测(经随机抽样查证,在1/3的预测靶区发现Au矿化),同时获得了与Au成矿地球化学理论结果高度一致的元素组合,并且对各元素在找矿靶区预测模型中的重要程度做出定量评价,为进一步研究矿床成因和控矿条件提供了定量依据。该研究结果充分说明海量数据信息中隐藏着极大的潜能,依据地质大数据的思想和定量研究方法就能将其充分地挖掘出来。同时这也充分证明了通过"查明数据间的相关关系取代分析事物因果关系"开展地质研究、找矿靶区定量优选的可行性和必要性。
Today,with rapid development of computer science and technology,ever more geologists are learning and applying big data based research methods,as it has become evident that many geological problems can be solved or clarified by analyzing correlativity of geological data.Here,we used multivariant statistical analysis method to mine the 1∶200000 scale geochemical survey data of stream sediments from western Qinling district,and established a series of quantitative optimization models of target areas for regional Au prospecting.These series of optimization models increased the accuracy for Au metallogenetic prediction in the study area(up to 30% Au prediction accuracy in randomly selected areas);meanwhile,they predicted the elemental composition of Au ore with high agreement with the theoretical value based on Au metallogenic geochemistry.We quantitatively evaluated each element according to its predicting power in all predication models to provide quantitative basis for further research on the genesis and controlling factors of Au ore deposit.Our results demonstrate that massive geological data possess great research potential which can only be exploited by applying big data and quantitative analytical methodologies.At the same time,it fully proved that the feasibility and necessity of quantitative optimization selection of geological research and exploration target area is realized by "identifying the relationship between data and replacing the causal relationship between things".
Today,with rapid development of computer science and technology,ever more geologists are learning and applying big data based research methods,as it has become evident that many geological problems can be solved or clarified by analyzing correlativity of geological data.Here,we used multivariant statistical analysis method to mine the 1∶200000 scale geochemical survey data of stream sediments from western Qinling district,and established a series of quantitative optimization models of target areas for regional Au prospecting.These series of optimization models increased the accuracy for Au metallogenetic prediction in the study area(up to 30% Au prediction accuracy in randomly selected areas);meanwhile,they predicted the elemental composition of Au ore with high agreement with the theoretical value based on Au metallogenic geochemistry.We quantitatively evaluated each element according to its predicting power in all predication models to provide quantitative basis for further research on the genesis and controlling factors of Au ore deposit.Our results demonstrate that massive geological data possess great research potential which can only be exploited by applying big data and quantitative analytical methodologies.At the same time,it fully proved that the feasibility and necessity of quantitative optimization selection of geological research and exploration target area is realized by "identifying the relationship between data and replacing the causal relationship between things".
引文
[1]张旗,周永章.大数据正在引发地球科学领域一场深刻的革命[J].地质科学,2017,52(3):637-648.
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[3] CARRANZA E J M.Controls on mineral deposit occurrence inferred from analysis of their spatial pattern and spatial association with geological features[J].Ore Geology Reviews,2009,35(3):383-400.
[4] WIGAN M R,CLARKE R.Big data's big unintended consequences[J].IEEE Computer,2013,46:46-53.
[5] FRANKEL F,REID R.Big data:distilling meaning from data[J].Nature,2008,455:30.
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[8]舍恩伯格,库克耶.盛杨燕,周涛译.大数据时代生活、工作与思维的大变革[M].杭州:浙江人民出版社,2013:1-267.
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[10] BENGIO Y.Learning deep architectures for AI[J].Foundations and Trends in Machine Learning,2009,2(1):1-127.
[11] GROSSNER K E,GOODCHILD M F,CLARKE K C.Defining a Digital Earth system[J].Transactions in GIS,2008,12:145-160.
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[13] HUBEL D H,WIESEL T N.Receptive fields,binocular interaction and functional architecture in the cat's visual cortex[J].Journal of Physiology,1962,160(1):106.
[14]郭华东,王长林.数字地球15年发展与前瞻[J].中国科学院院刊,2013,28(增刊):59-66.
[15]王登红,刘新星,刘丽君.地质大数据的特点及其在成矿规律、成矿系列研究中的应用[J].矿床地质,2015,34(6):1143-1154.
[16]李国杰,程学旗.大数据研究:未来科技及经济社会发展的重大战略领域:大数据的研究现状与科学思考[J].中国科学院院刊,2012,27:647-657.
[17]罗建民,王晓伟,宋秉田,等.岩浆岩定量分类方法探讨:甘肃省西秦岭地区为例[J].岩石学报,2018,34(2):326-332.
[18]罗建民,张琪,宋秉田,等.物化探信息综合处理在找矿靶区定量优选中的应用[J].矿物岩石地球化学通报,2017,36(6):886-890.
[19]张旗,周永章.大数据时代对科学研究方法的反思:《矿物岩石地球化学通报》2017大数据专辑代序[J].矿物岩石地球化学通报,2017,52(3):1-12.
[20]周永章,陈烁,张旗,等.大数据与数学地球科学研究进展:大数据与数学地球科学专题代序[J].岩石学报,2018,34(2):255-263.
[21]张新虎,刘建宏,梁明宏,等.甘肃省区域成矿及找矿[M].北京:地质出版社,2013:562-576.
[22] LI N,DENG J,YANG L Q,et al.Constraints on depositional conditions and ore-fluid source for orogenic gold districts in the West Qinling Orogen,China:implications from sulfide assemblages and their trace-element geochemistry[J].Ore Geology Reviews,2018,102:204-219.https:∥doi.org/10.1016/j.oregeorev.2018.08.025.
[23] WU G L,MENG Q R,DUAN L,et al.Early Mesozoic structural evolution of the eastern West Qinling,Northwest China[J].Tectonophysics,2014,630:9-20.
[24] LI L,MENG Q R,PULLEN A,et al.Late Permian-early Middle Triassic back-arc basin development in West Qinling,China[J].Journal of Asian Earth Sciences,2014,87:116-129.
[25] SUI J X,LI J W,WEN G,et al.The Dewulu reduced AuCu skarn deposit in the Xiahe-Hezuo district,West Qinling Orogen,China:implications for an intrusion-related gold system[J].Ore Geology Reviews,2017,80:1230-1244.
[26]侯云生,张新虎,彭德启.甘肃省黄金资源及可持续发展[M].兰州:甘肃科学技术出版社,2004:64-71.
[27]韩海涛,刘继顺,董新,等.西秦岭温泉斑岩型钼矿床地质特征及成因浅析[J].地质与勘探,2008,44(4):1-7.
[28]金维浚,张旗,何登发,等.西秦岭埃达克岩的SHRIMP定年及其构造意义[J].岩石学报,2005,21(3):959-966.
[29]梁亚忠.甘肃武山温泉钼矿床地质特征[J].甘肃地质,2008,17(2):40-49.
[30]邱庆伦,龚全胜,卢书伟,等.甘肃夏河地区印支期埃达克岩的厘定及其意义[J].甘肃地质,2008,17(3):6-12.
[31]殷勇,赵彦庆.甘肃西秦岭金矿富集区花岗岩与金成矿作用的关系[J].甘肃地质,2006,15(1):36-41.
[32]张宏飞,靳兰兰,张利,等.西秦岭花岗岩类地球化学和PbSr-Nd同位素组成对基底性质及其构造属性的限制[J].中国科学:D辑,2005,35(10):914-926.
[33]罗建民,侯云山,张新虎,等.甘肃省岩金资源预测及祁连评价模型[J].矿床地质,2005,25(7):53-59.
[34]罗建民,张新虎,彭德启.甘肃省岩金矿品位-储量分布模型[J].地质与勘探,2005,41(5):62-65.
[35]张旗,殷先明,殷勇,等.西秦岭与埃达克岩和喜马拉雅型花岗岩有关的金铜成矿及找矿问题[J].岩石学报,2009,31(2):3103-3122.
[36]池顺都.矿产勘查学简明教程[M].北京:地质出版社,2012:20-61.
[37]李景春.金在热液流体中存在形式的讨论:“纳米效应”在成矿理论中的应用[J].地质与资源,1995,4(4):307-311.
[38]罗云之,罗建民,王孝通.加密水系沉积物测量在快速缩小找矿靶区中的应用[J].甘肃地质,2018,27(1):37-42.
[39]赵鹏大,胡旺亮,李紫金.矿床统计预测[M].北京:地质出版社,1994:144-161.
[40]刘艳.关于黄金分割法的几点讨论[J].机电技术,2006(1):13-14,59.
[41]赵鹏大.大数据时代数字找矿与定量评价[J].地质通报,2015,34(7):1255-1259.
[42]朱月琴,谭永杰,张建通,等.基于Hadoop的地质大数据融合与挖掘技术框架[J].测绘学报,2015,44(增刊):152-159.
[43]谭永杰,文敏,朱月琴,等.地质数据的大数据特性研究[J].中国矿业,2017,26(9):67-84.
[44]吴冲龙,刘刚,张夏林,等.地质科学大数据及其利用的若干问题探讨[J].科学通报,2016,61(16):1797-1807.
[2] ORD A,HOBBS B E,ZHAO C B.Fundamentals of computational geoscience[J].Lecture Notes in Earth Sciences,2009,122(3):1-257.
[3] CARRANZA E J M.Controls on mineral deposit occurrence inferred from analysis of their spatial pattern and spatial association with geological features[J].Ore Geology Reviews,2009,35(3):383-400.
[4] WIGAN M R,CLARKE R.Big data's big unintended consequences[J].IEEE Computer,2013,46:46-53.
[5] FRANKEL F,REID R.Big data:distilling meaning from data[J].Nature,2008,455:30.
[6] SILVER D,SCHRITTWIESER J,SIMONYAN K,et al.Mastering the game of go without human knowledge[J].Nature,2017,550:354.
[7]孟小峰,慈祥.大数据管理:概念、技术与挑战[J].计算机研究与发展,2013,50(1):146-169.
[8]舍恩伯格,库克耶.盛杨燕,周涛译.大数据时代生活、工作与思维的大变革[M].杭州:浙江人民出版社,2013:1-267.
[9]郭华东.科学大数据驱动地学学科发展[J].科技导报,2018,36(5):1.
[10] BENGIO Y.Learning deep architectures for AI[J].Foundations and Trends in Machine Learning,2009,2(1):1-127.
[11] GROSSNER K E,GOODCHILD M F,CLARKE K C.Defining a Digital Earth system[J].Transactions in GIS,2008,12:145-160.
[12] GUO H D.China's Earth observing satellites for building a Digital Earth[J].International Journal of Digital Earth,2012,5:185-188.
[13] HUBEL D H,WIESEL T N.Receptive fields,binocular interaction and functional architecture in the cat's visual cortex[J].Journal of Physiology,1962,160(1):106.
[14]郭华东,王长林.数字地球15年发展与前瞻[J].中国科学院院刊,2013,28(增刊):59-66.
[15]王登红,刘新星,刘丽君.地质大数据的特点及其在成矿规律、成矿系列研究中的应用[J].矿床地质,2015,34(6):1143-1154.
[16]李国杰,程学旗.大数据研究:未来科技及经济社会发展的重大战略领域:大数据的研究现状与科学思考[J].中国科学院院刊,2012,27:647-657.
[17]罗建民,王晓伟,宋秉田,等.岩浆岩定量分类方法探讨:甘肃省西秦岭地区为例[J].岩石学报,2018,34(2):326-332.
[18]罗建民,张琪,宋秉田,等.物化探信息综合处理在找矿靶区定量优选中的应用[J].矿物岩石地球化学通报,2017,36(6):886-890.
[19]张旗,周永章.大数据时代对科学研究方法的反思:《矿物岩石地球化学通报》2017大数据专辑代序[J].矿物岩石地球化学通报,2017,52(3):1-12.
[20]周永章,陈烁,张旗,等.大数据与数学地球科学研究进展:大数据与数学地球科学专题代序[J].岩石学报,2018,34(2):255-263.
[21]张新虎,刘建宏,梁明宏,等.甘肃省区域成矿及找矿[M].北京:地质出版社,2013:562-576.
[22] LI N,DENG J,YANG L Q,et al.Constraints on depositional conditions and ore-fluid source for orogenic gold districts in the West Qinling Orogen,China:implications from sulfide assemblages and their trace-element geochemistry[J].Ore Geology Reviews,2018,102:204-219.https:∥doi.org/10.1016/j.oregeorev.2018.08.025.
[23] WU G L,MENG Q R,DUAN L,et al.Early Mesozoic structural evolution of the eastern West Qinling,Northwest China[J].Tectonophysics,2014,630:9-20.
[24] LI L,MENG Q R,PULLEN A,et al.Late Permian-early Middle Triassic back-arc basin development in West Qinling,China[J].Journal of Asian Earth Sciences,2014,87:116-129.
[25] SUI J X,LI J W,WEN G,et al.The Dewulu reduced AuCu skarn deposit in the Xiahe-Hezuo district,West Qinling Orogen,China:implications for an intrusion-related gold system[J].Ore Geology Reviews,2017,80:1230-1244.
[26]侯云生,张新虎,彭德启.甘肃省黄金资源及可持续发展[M].兰州:甘肃科学技术出版社,2004:64-71.
[27]韩海涛,刘继顺,董新,等.西秦岭温泉斑岩型钼矿床地质特征及成因浅析[J].地质与勘探,2008,44(4):1-7.
[28]金维浚,张旗,何登发,等.西秦岭埃达克岩的SHRIMP定年及其构造意义[J].岩石学报,2005,21(3):959-966.
[29]梁亚忠.甘肃武山温泉钼矿床地质特征[J].甘肃地质,2008,17(2):40-49.
[30]邱庆伦,龚全胜,卢书伟,等.甘肃夏河地区印支期埃达克岩的厘定及其意义[J].甘肃地质,2008,17(3):6-12.
[31]殷勇,赵彦庆.甘肃西秦岭金矿富集区花岗岩与金成矿作用的关系[J].甘肃地质,2006,15(1):36-41.
[32]张宏飞,靳兰兰,张利,等.西秦岭花岗岩类地球化学和PbSr-Nd同位素组成对基底性质及其构造属性的限制[J].中国科学:D辑,2005,35(10):914-926.
[33]罗建民,侯云山,张新虎,等.甘肃省岩金资源预测及祁连评价模型[J].矿床地质,2005,25(7):53-59.
[34]罗建民,张新虎,彭德启.甘肃省岩金矿品位-储量分布模型[J].地质与勘探,2005,41(5):62-65.
[35]张旗,殷先明,殷勇,等.西秦岭与埃达克岩和喜马拉雅型花岗岩有关的金铜成矿及找矿问题[J].岩石学报,2009,31(2):3103-3122.
[36]池顺都.矿产勘查学简明教程[M].北京:地质出版社,2012:20-61.
[37]李景春.金在热液流体中存在形式的讨论:“纳米效应”在成矿理论中的应用[J].地质与资源,1995,4(4):307-311.
[38]罗云之,罗建民,王孝通.加密水系沉积物测量在快速缩小找矿靶区中的应用[J].甘肃地质,2018,27(1):37-42.
[39]赵鹏大,胡旺亮,李紫金.矿床统计预测[M].北京:地质出版社,1994:144-161.
[40]刘艳.关于黄金分割法的几点讨论[J].机电技术,2006(1):13-14,59.
[41]赵鹏大.大数据时代数字找矿与定量评价[J].地质通报,2015,34(7):1255-1259.
[42]朱月琴,谭永杰,张建通,等.基于Hadoop的地质大数据融合与挖掘技术框架[J].测绘学报,2015,44(增刊):152-159.
[43]谭永杰,文敏,朱月琴,等.地质数据的大数据特性研究[J].中国矿业,2017,26(9):67-84.
[44]吴冲龙,刘刚,张夏林,等.地质科学大数据及其利用的若干问题探讨[J].科学通报,2016,61(16):1797-1807.
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