基于深度学习的深层次矿化信息挖掘与集成

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英文篇名：Deep Learning-Based Mining and Integration of Deep-Level Mineralization Information 作者：左仁广 英文作者：ZUO Ren-guang;State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences (Wuhan); 关键词：矿产预测 ; 深层次矿化信息 ; 大数据 ; 深度学习 英文关键词：mineral exploration;;deep-level mineralization information;;big data;;deep learning 中文刊名：矿物岩石地球化学通报 英文刊名：Bulletin of Mineralogy,Petrology and Geochemistry 机构：中国地质大学(武汉)地质过程与矿产资源国家重点实验室; 出版日期：2019-01-10 出版单位：矿物岩石地球化学通报 年：2019 期：01 基金：国家自然科学基金项目(41772344,41522206);; 湖北省自然科学基金项目(2017CFA053) 语种：中文; 页：57-64+207 页数：9 CN：52-1102/P ISSN：1007-2802 分类号：P624

摘要

矿产预测的核心是对地学空间数据进行特征提取与集成融合,当前的研究热点和前沿聚焦于深层次矿化信息特征提取与集成。进入大数据时代,如何基于机器学习开展深层次矿化信息挖掘与集成是当前矿产预测的前沿领域。本文介绍了基于机器学习的矿产预测与评价研究的主要内容,深度学习的基本原理,以及深度学习在地球化学异常识别和多源找矿信息集成融合中的应用。研究结果表明,深度学习可有效识别和提取地球化学异常,并能对地质、地球物理、地球化学等多源地学数据进行特征提取、集成融合及找矿远景区圈定。尽管如此,如何把深度学习与地质约束有机结合,使其既能有效挖掘与集成深层次矿化信息,又符合地质认知,还需要更加深入的研究。
        The core of mineral resources prediction and assessment is the feature extraction and integration of various geospatial data. The current research hotspot and frontier focus on the feature extraction and integration of the deep-level mineralization information. In the era of big data,how to carry out deep mining and integration of deep-level mineralization information based on machine learning algorithms is one of the frontier subjects in current mineral exploration. This paper briefly introduces the main research contents of mineral prediction and evaluation based on machine learning algorithms,the basic principle of deep learning algorithms,and the application of deep learning algorithms in the recognition of geochemical anomalies and integration of multi-source prospecting information. The results demonstrate that the deep learning algorithms can effectively identify and extract geochemical anomalies,and can extract characteristics and integrate geological, geophysical, geochemical and other multi-source geoscientific data, and delineate the prospecting area.Nevertheless,how to combine deep learning algorithms with geological constraints so that they can not only effectively excavate and integrate deep-level mineralization information,but also coincide with geological cognition,and more in-depth research is needed.

引文

Agterberg F P.1989.Computer programs for mineral exploration.Science,245(4913):76-81
    Aryafar A,Moeini H.2017.Application of continuous restricted Boltzmann machine to detect multivariate anomalies from stream sediment geochemical data,Korit,East of Iran.Journal of Mining and Environment,8(4):673-682
    Cargill S M,Clark A L.1978.Report on the activity of IGCP Project 98.Journal of the International Association for Mathematical Geology,10(5):411-417
    Carranza E J M,Laborte A G.2015.Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra(Philippines).Computers&Geosciences,74:60-70
    Chen Y L,Lu L J,Li X B.2014a.Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly.Journal of Geochemical Exploration,140:56-63
    Chen Y L.2015.Mineral potential mapping with a restricted Boltzmann machine.Ore Geology Reviews,71:749-760
    Chen Y S,Lin Z H,Zhao X,Wang G,Gu Y F.2014b.Deep learningbased classification of hyperspectral data.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,7(6):2094-2107
    Cheng Q M.2012.Singularity theory and methods for mapping geochemical anomalies caused by buried sources and for predicting undiscovered mineral deposits in covered areas.Journal of Geochemical Exploration,122:55-70
    de Mulder E F J,Cheng Q M,Agterberg F,Goncalves M.2016.New and game-changing developments in geochemical exploration.Episodes,39:70-71
    Gao Y,Zhang Z J,Xiong Y H,Zuo R G.2016.Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province,China.Ore Geology Reviews,75:16-28
    Guo H D,Wang L Z,Chen F,Liang D.2014.Scientific big data and digital earth.Chinese Science Bulletin,59(35):5066-5073
    Hinton G E.2002.Training products of experts by minimizing contrastive divergence.Neural Computation,14(8):1771-1800
    Hinton G E,Salakhutdinov R R.2006.Reducing the dimensionality of data with neural networks.Science,313(5786):504-507
    Hinton G E,Osindero S,Teh Y W.2006.A fast learning algorithm for deep belief nets.Neural Computation,18(7):1527-1554
    Hu W,Huang Y Y,Wei L,Zhang F,Li H C.2015.Deep convolutional neural networks for hyperspectral image classification.Journal of Sensors,2015:258619
    Jiang G Q,Xu J,Wei J.2018.A deep learning algorithm of neural network for the parameterization of typhoon-ocean feedback in typhoon forecast models.Geophysical Research Letters,45(8):3706-3716
    Li T W,Shen H F,Yuan Q Q,Zhang X C,Zhang L P.2017a.Estimating ground-level PM2.5by fusing satellite and station observations:A geo-intelligent deep learning approach.Geophysical Research Letters,44(23):11985-11993
    Li W,Wu G D,Du Q.2017b.Transferred deep learning for anomaly detection in hyperspectral imagery.IEEE Geoscience and Remote Sensing Letters,14(5):597-601
    Li W J,Fu H H,Yu L,Gong P,Feng D L,Li C C,Clinton N.2016.Stacked autoencoder-based deep learning for remote-sensing image classification:A case study of African land-cover mapping.International Journal of Remote Sensing,37(23):5632-5646
    Luus F P S,Salmon B P,Van den Bergh F,Maharaj B T J.2015.Multiview deep learning for land-use classification.IEEE Geoscience and Remote Sensing Letters,12(12):2448-2452
    Mayer-Sch9nberger V,Cukier K.2013.Big data:A revolution that will transform how we live,work,and think.Boston:Houghton Mifflin Harcourt
    Moeini H,Torab F M.2017.Comparing compositional multivariate outliers with autoencoder networks in anomaly detection at Hamich exploration area,east of Iran.Journal of Geochemical Exploration,180:15-23
    Nyknen V.2008.Radial basis functional link nets used as a prospectivity mapping tool for orogenic gold deposits within the Central Lapland Greenstone Belt,Northern Fennoscandian Shield.Natural Resources Research,17(1):29-48
    Ross Z E,Meier M A,Hauksson E.2018.P wave arrival picking and first-motion polarity determination with deep learning.Journal of Geophysical Research,123(6):5120-5129,
    Singer D A,Kouda R.1996.Application of a feedforward neural network in the search for Kuroko deposits in the Hokuroku district,Japan.Mathematical Geology,28(8):1017-1023
    Valentine A P,Trampert J.2012.Data space reduction,quality assessment and searching of seismograms:Autoencoder networks for waveform data.Geophysical Journal International,189(2):1183-1202
    Xiong Y H,Zuo R G.2016.Recognition of geochemical anomalies using a deep autoencoder network.Computers&Geosciences,86:75-82
    Xiong Y H,Zuo R G,Carranza E J M.2018.Mapping mineral prospectivity through big data analytics and a deep learning algorithm.Ore Geology Reviews,102:811-817
    Zhang L P,Zhang L F,Du B.2016a.Deep learning for remote sensing data:A technical tutorial on the state of the art.IEEE Geoscience and Remote Sensing Magazine,4(2):22-40
    Zhang Z J,Zuo R G,Xiong Y H.2016b.A comparative study of fuzzy weights of evidence and random forests for mapping mineral prospectivity for skarn-type Fe deposits in the southwestern Fujian metallogenic belt,China.Science China Earth Sciences,59(3):556-572
    Zuo R G,Carranza E J M.2011.Support vector machine:A tool for mapping mineral prospectivity.Computers&Geosciences,37(12):1967-1975
    Zuo R G.2017.Machine learning of mineralization-related geochemical anomalies:A review of potential methods.Natural Resources Research,26(4):457-464
    Zuo R G,Xiong Y H.2018.Big data analytics of identifying geochemical anomalies supported by machine learning methods.Natural Resources Research,27(1):5-13
    陈建平,李婧,崔宁,于萍萍.2015.大数据背景下地质云的构建与应用.地质通报,34(7):1260-1265
    陈永清,赵鹏大.2009.综合致矿地质异常信息提取与集成.地球科学---中国地质大学学报,34(2):325-335
    罗建民,张琪,宋秉田,王晓伟,杨忠明,赵彦庆,刘升有.2017.物化探信息综合处理在找矿靶区定量优选中的应用.矿物岩石地球化学通报,36(6):886-890
    谭永杰.2016.地质大数据体系建设的总体框架研究.中国地质调查,3(3):1-6
    王登红,刘新星,刘丽君.2015.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.矿床地质,34(6):1143-1154
    吴冲龙,刘刚,张夏林,何珍文,张志庭.2016.地质科学大数据及其利用的若干问题探讨.科学通报,61(16):1797-1807
    肖克炎,李楠,王琨,孙莉,范建福,丁建华.2015.大数据思维下的矿产资源评价.地质通报,34(7):1266-1272
    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
    翟明国.2017.花岗岩:大陆地质研究的突破口以及若干关键科学问题:---“岩石学报”花岗岩专辑代序.岩石学报,33(5):1369-1380
    张旗,周永章.2017.大数据正在引发地球科学领域一场深刻的革命---《地质科学》2017年大数据专题代序.地质科学,52(3):637-648
    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,黎培兴,王树功,肖凡,李景哲,高乐.2017.矿床大数据及智能矿床模型研究背景与进展.矿物岩石地球化学通报,36(2):327-331,344
    周永章,陈铄,张旗,肖凡,王树功,刘艳鹏,焦守涛.2018a.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.岩石学报,34(2):255-263
    周永章,张良均,张奥多,王俊.2018b.地球科学大数据挖掘与机器学习.中山大学出版社
    左仁广.2018.勘查地球化学数据挖掘与弱异常识别.地学前缘.doi:10.13745/j.esf.sf.2 018.6.25