大数据开创地学研究新途径:查明相关关系,增强研究可行性
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摘要
人类已经进入大数据时代,大数据研究的思想、方法在地学领域也备受关注。笔者认为,大数据研究的对象是数据,研究的工具是计算机,研究的方法、手段是查明数据间相关关系,研究的特点是取向高概率做出决策。大数据是通过对大量数据的挖掘,查明数据间的相关关系,研究问题并做出正确决策的思想、方法。本文提出大数据是应用"归纳法"开展科学研究的思想、方法,以及高性能计算机和大数据计算技术使"归纳法"得以升华的观点。文章通过对统计学、机器学习算法的深入探讨,得出大数据将改变人们对自然的理解和认知方式,改变科学研究的思想和方法,改变长期以来人们通过查找因果关系开展科学研究的习惯。大数据必将开创一条跨越复杂的因果关系、直接获得研究结果的全新的科学研究途径。随着数据爆发式增长,随着高性能计算机的普及和计算技术的迅猛发展,统计分析方法将很大程度地突破数据体量的限制,统计分析预测模型以其真实可靠的处理结果、对条件和结果良好的解释能力、结合机器学习算法对半结构化与非结构化数据的处理优势,将推动地质科学进入定量化研究的新高度。
Humans have entered the era of big data.Research ideas and methods based on big data have gained much attention and start to apply widely in the field of geoscience.In our view,the subject of big data research is data,the tool is the computer,the method and means are to find out the correlation between data,and the characteristics is to make decisions based on probability criteria.To reiterate:big data is the idea and method of finding out the correlation between data;it researches problems and make correct decisions by mining large amounts of data.In this paper,we suggest that the inductive method is the way to carry out big data research,specially as its research power has been greatly enhanced by high performance computer and big data technology.Through an in-depth analyses of statistics and machine learning algorithm,we came to the conclusion that big data shall change the ways people learn and understand nature and scientific studies are designed and performed.And it shall subvert the long-standing habit of conducting scientific research by finding causal relationships.Big data shall create a new approach to conducting geoscience research across complex causal relationships and obtaining research results directly.We concluded in this study that with the explosive growth of data,and with popularization of high-performance computers and rapid development of computing technology,the statistical analysis method has largely broken through the limitation of data volume.This shall enable statistical analysis and prediction models to generate truer thus more reliable results.Ultimately,the ability to explain conditions and outcomes,combining with the advantages of machine learning algorithms for semi-structured and unstructured data,will make quantitative geoscience research truly feasible.
Humans have entered the era of big data.Research ideas and methods based on big data have gained much attention and start to apply widely in the field of geoscience.In our view,the subject of big data research is data,the tool is the computer,the method and means are to find out the correlation between data,and the characteristics is to make decisions based on probability criteria.To reiterate:big data is the idea and method of finding out the correlation between data;it researches problems and make correct decisions by mining large amounts of data.In this paper,we suggest that the inductive method is the way to carry out big data research,specially as its research power has been greatly enhanced by high performance computer and big data technology.Through an in-depth analyses of statistics and machine learning algorithm,we came to the conclusion that big data shall change the ways people learn and understand nature and scientific studies are designed and performed.And it shall subvert the long-standing habit of conducting scientific research by finding causal relationships.Big data shall create a new approach to conducting geoscience research across complex causal relationships and obtaining research results directly.We concluded in this study that with the explosive growth of data,and with popularization of high-performance computers and rapid development of computing technology,the statistical analysis method has largely broken through the limitation of data volume.This shall enable statistical analysis and prediction models to generate truer thus more reliable results.Ultimately,the ability to explain conditions and outcomes,combining with the advantages of machine learning algorithms for semi-structured and unstructured data,will make quantitative geoscience research truly feasible.
引文
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[17]施俊法,唐金荣,周平,等.找矿模型与矿产勘查[M].北京:地质出版社,2010:12-40.
[18]里奇.西方矿产勘查哲学[M].北京:中国地质矿产信息研究院报道室,1992:157-168.
[19]江大白,徐飞.大数据:科学方法的新变革[J].自然辩证法研究,2016,32(1):109-114.
[20]赵鹏大,胡旺亮,李紫金.矿床统计预测[M].北京:地质出版社,1994:125-179.
[21]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.
[22]王晓伟,罗建民,王玉玺,等.花岗岩定量分类方法及与成矿空间相关关系分析:甘肃省西秦岭地区为例[J].甘肃地质,2018,27(1):27-31.
[23] HINTON G E,OSINDERO S,TEH Y W.A fast learning algorithm for deep belief nets[J].Neural Computation,2006,18(7):1527.
[24] FUKUSHIMA K,MIYAKE S.Neocognitron:a new algorithm for pattern recognition tolerant of deformations and shifts in position[J].Pattern Recognition,1982,15(6):455-469.
[2]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.
[3] ORD A,HOBBS B E,ZHAO C.Fundamentals of computational geoscience[J].Lecture Notes in Earth Sciences,2009,122(3):1-257.
[4]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.
[5]郭华东.大数据大科学大发现:大数据与科学发现国际研讨会综述[J].中国科学院院刊,2014,29(4):500-506.
[6]罗建民,张旗.大数据开创地学研究新途径:全数据挖掘提高研究精度[J].地质科学,2018,53(4):1-8.
[7] GENUER R,POGGI J M,TULEAU-MALOT C.Variable selection using random forests[J].Pattern Recognition Letters,2010,31(14):2225-2236.
[8] WIGAN M R,CLARKE R.Big data's big unintended consequences[J].IEEE Computer,2013,46:46-53.
[9] SILVER D,SCHRITTWIESER J,SIMONYAN K,et al.Mastering the game of Go without human knowledge[J].Nature,2017,550(7676):354.
[10]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,2013,1:146-169.
[11]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.
[12]李国杰,程学旗.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.
[13]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.
[14]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].岩石矿物学通报,2017,36(6):886-890.
[15]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.
[16]周永章,张良均,张奥多,等.地球科学大数据挖掘与机器学习[M].广州:中山大学出版社,2018:1-272.
[17]施俊法,唐金荣,周平,等.找矿模型与矿产勘查[M].北京:地质出版社,2010:12-40.
[18]里奇.西方矿产勘查哲学[M].北京:中国地质矿产信息研究院报道室,1992:157-168.
[19]江大白,徐飞.大数据:科学方法的新变革[J].自然辩证法研究,2016,32(1):109-114.
[20]赵鹏大,胡旺亮,李紫金.矿床统计预测[M].北京:地质出版社,1994:125-179.
[21]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.
[22]王晓伟,罗建民,王玉玺,等.花岗岩定量分类方法及与成矿空间相关关系分析:甘肃省西秦岭地区为例[J].甘肃地质,2018,27(1):27-31.
[23] HINTON G E,OSINDERO S,TEH Y W.A fast learning algorithm for deep belief nets[J].Neural Computation,2006,18(7):1527.
[24] FUKUSHIMA K,MIYAKE S.Neocognitron:a new algorithm for pattern recognition tolerant of deformations and shifts in position[J].Pattern Recognition,1982,15(6):455-469.
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