矿床大数据及智能矿床模型研究背景与进展
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摘要
大数据是"未来的新石油",Nature和Science相继出版专刊来探讨大数据带来的挑战和机遇。大数据的特征是数据规模大,并经常呈异构多模态、复杂关联、动态涌现等特点,需要高效计算模型和方法。大数据-智能矿床研究刚刚起步,需对多维、异构、隐性大数据的高效存储、管理、集成、融合与深度挖掘,需人工智能方法——机器学习、深度学习、可视分析的应用。贝叶斯网络是成因建模的一个革命性工具,可以用来揭示矿床的成因机制及它们背后的规律。来自地质调查、监测数据获得的与"矿"有关的大数据,通过迭代计算,可以不断完善所建立的矿床模型,并且通过云计算技术,使得世界各地的矿床研究团队共同参与,引发矿床模型研究方式的变革。
Big data is the key resource in the future.Both Nature and Science have published special issues to discuss the challenge and opportunity brought by big data.Big data are characterized by large scale,as well as by heterogeneous multi-modal,complicated interaction,dynamic emergence and others,so that highly efficient computing methods and models are required.Study on big data-based intelligent mineral deposit has just started,pushing for efficient storage and management,integration,fusion and deep mining of multi-dimensional,heterogeneous and recessive big data,as well as for the application of artificial intelligence methods such as machine learning,in-depth learning and visual analysis.The Bayesian network is a revolutionary tool for genetic modeling.It can be used to reveal the genetic mechanism of mineral deposits and the patterns behind them.Mineral depositrelated big data from geological survey and monitoring data could constantly improve the deposit model through continuing iterative calculation.Furthermore,the cloud computing technology makes it possible to concurrently study a deposit model by research teams from all over the world at the same time,and thus leads to a revolution of the research mode of deposit model.
Big data is the key resource in the future.Both Nature and Science have published special issues to discuss the challenge and opportunity brought by big data.Big data are characterized by large scale,as well as by heterogeneous multi-modal,complicated interaction,dynamic emergence and others,so that highly efficient computing methods and models are required.Study on big data-based intelligent mineral deposit has just started,pushing for efficient storage and management,integration,fusion and deep mining of multi-dimensional,heterogeneous and recessive big data,as well as for the application of artificial intelligence methods such as machine learning,in-depth learning and visual analysis.The Bayesian network is a revolutionary tool for genetic modeling.It can be used to reveal the genetic mechanism of mineral deposits and the patterns behind them.Mineral depositrelated big data from geological survey and monitoring data could constantly improve the deposit model through continuing iterative calculation.Furthermore,the cloud computing technology makes it possible to concurrently study a deposit model by research teams from all over the world at the same time,and thus leads to a revolution of the research mode of deposit model.
引文
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周永章,王正海.侯卫生.2012.数学地球科学.广州:中山大学出版社,247
郭华东,王力哲,陈方,梁栋.2015.科学大数据与数字地球.科学通报,59(12):1047-1054
李超岭,李健强,张宏春,龚爱华,魏东琦.2015.智能地质调查大数据应用体系架构与关键技术.地质通报,34(7):1288-1299
施俊法,唐金荣,周平,郑军卫.2014.世界地质调查工作发展趋势及其对中国的启示.地质通报,33(10):1465-1472
吴冲龙,刘刚.2015.“玻璃地球”建设的现状、问题、趋势与对策.地质通报,34(7):1280-1287
严光生,薛群威,肖克炎,陈建平,缪谨励,余海龙.2015.地质调查大数据研究的主要问题分析.地质通报,34(7):1273-1279
杨宗喜,唐金荣,周平,张涛,金玺.2013.大数据时代下美国地质调查局的科学新观.地质通报,32(9):1337-1343
赵国栋,易欢欢,糜万军,鄂维南.2013.大数据时代的历史机遇.北京:清华大学出版社
赵鹏大.2015.大数据时代数字找矿与定量评价.地质通报,34(7):1255-1259
周永章,王正海.侯卫生.2012.数学地球科学.广州:中山大学出版社,247
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