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任梦姣, 孙记红, 王晓宇, 容伊霖, 白永良. 海洋地质数据挖掘系统研发及在海山年龄预测中的应用[J]. 海洋地质前沿, 2023, 39(2): 79-85. doi: 10.16028/j.1009-2722.2022.251
引用本文: 任梦姣, 孙记红, 王晓宇, 容伊霖, 白永良. 海洋地质数据挖掘系统研发及在海山年龄预测中的应用[J]. 海洋地质前沿, 2023, 39(2): 79-85. doi: 10.16028/j.1009-2722.2022.251
shu
REN Mengjiao, SUN Jihong, WANG Xiaoyu, RONG Yilin, BAI Yongliang. Marine geological data mining system development and its application in seamount age prediction[J]. Marine Geology Frontiers, 2023, 39(2): 79-85. doi: 10.16028/j.1009-2722.2022.251
Citation: REN Mengjiao, SUN Jihong, WANG Xiaoyu, RONG Yilin, BAI Yongliang. Marine geological data mining system development and its application in seamount age prediction[J]. Marine Geology Frontiers, 2023, 39(2): 79-85. doi: 10.16028/j.1009-2722.2022.251
shu

海洋地质数据挖掘系统研发及在海山年龄预测中的应用

  • 1.

    中国石油大学（华东）海洋与空间信息学院，青岛，266580

  • 2.

    中国地质调查局青岛海洋地质研究所，青岛，266237

  • 基金项目: 中国地质调查局项目（DD20190214，DD20221711）；国家自然科学基金面上项目（42176068）；山东省自然科学基金面上项目（ZR2020MD065）
详细信息
    作者简介: 任梦姣（1997—），女，在读硕士，主要从事智能海洋地质方面的研究工作. E-mail：renmail1105@163.com
    通讯作者: 孙记红（1984—），男，硕士，高级工程师，主要从事海洋地质大数据及智能化应用方面的研究工作. E-mail：sjihong@mail.cgs.gov.cn

  • 中图分类号: P628.4

Marine geological data mining system development and its application in seamount age prediction

  • 1.

    College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China

  • 2.

    Qingdao Institute of Marine Geology, China Geological Survey, Qingdao 266237, China

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    摘要

  • 利用数据挖掘技术分析海洋地质调查数据，以获取其中隐藏信息，对推进海洋地质数据的科学高效利用具有重要意义。在模块化设计原则下，利用Python语言开发海洋地质数据挖掘相关的核心功能，利用WinForm搭建人机交互界面，并通过参数交互的方式实现了界面和后台功能间的互动。基于综合地质地球物理资料，利用软件预测了太平洋海山年龄。预测结果精度高于利用传统克里金插值方法所得结果的精度。应用结果表明，所开发软件的数据预处理、指标分析、综合评价等功能具有很好的实用性。

    Using data mining technology to find hidden information in the marine exploration data is important for increasing the marine data using efficiently. The core functions of marine geological data mining system (MGDMS) was developed in the Python, the graphical user interface (GUI) was designed in the WinForm, and finally the interaction between GUI and the core functions was realized via parameter transformation and transference. Based on the geological and geophysical data, the ages of seamount in the Pacific are predicted using the MGDMS, and the predicted ages are more precise than those of conventional Kriging method. Our case study result indicates that the functions of data preprocessing, index analysis, and overall evaluation with the MGDMS are suitable for marine geophysical data mining.

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  • 图 1  海洋地质调查数据情况

    Figure 1. 

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    图 2  海洋地质数据挖掘系统总体架构

    Figure 2. 

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    图 3  太平洋地形[40]、海山与热流站位分布[42]

    Figure 3. 

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    图 4  基于GA-BP模型的海山年龄实测值与预测值对比

    Figure 4. 

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    图 5  基于测试集的GA-BP海山年龄预测值与实测值散点图

    Figure 5. 

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    表 1  用于太平洋海山年龄预测的基础数据源

    Table 1.  Data sources for the Pacific seamount age prediction

    数据名称分辨率/弧分发布人备注
    空间重力异常1SANDWELL等[36]卫星测高数据为基础
    磁力异常2MAUS等 [37]4 km高度处的磁异常
    洋壳年龄2MÜLLER等 [38]
    沉积层厚度5STRAUME等[39]
    水深与地形1AMANTE等 [40]
    海底热流站位数据国际热流委员会
    海山年龄点位数据V CLOUARD AND A BONNEVILLE [41]前人成果的汇编
    下载: 导出CSV

    表 2  与海山年龄相关地球物理数据的MIV绝对值

    Table 2.  The absolute mean impact values of the seamount-age related geophysical observables

    地球物理观测值数据类型处理方法|MIV|/Ma
    洋壳年龄网格提取9.23
    重力异常网格提取3.50
    海山高度网格提取1.42
    沉积层厚度网格提取1.08
    磁异常网格提取0.82
    海底热流地点内插0.21
    下载: 导出CSV

    表 3  克里金插值算法、BP和GA-BP算法预测精度对比

    Table 3.  Comparison in prediction accuracy among the Kriging, BP, and GA-BP algorithms

    预测模型RMSE/MaR²
    克里金27.540.60
    BP模型20.350.74
    GA-BP模型9.690.93
    下载: 导出CSV
    • 参考文献(45)
  • [1]

    LI W L,GAO S Y,HAN C H,et al. A brief analysis on data mining for deep-sea mineral resources based on big data[J]. Procedia Computer Science,2019,154:699-705. doi: 10.1016/j.procs.2019.06.109

    [2]

    杜艳玲. 混合云存储环境下海洋大数据的布局及迁移算法研究[D]. 上海: 上海海洋大学, 2014.

    [3]

    JIANG K,JIANG X. Technological development of ocean mineral resources exploitation[J]. Nonferrous Metals Engineering,2011,1(1):3-8.

    [4]

    HE G,DENG X,YANG S. Geological characteristics of polymetallic nodules in the central Indian Ocean and comparison with those from CC Zone in the eastern Pacific[J]. Marine Geology Quaternary Geology,2011,31(2):21-30. doi: 10.3724/SP.J.1140.2011.02021

    [5]

    HAND D J. Principles of data mining[J]. Drug safety,2007,30(7):621-622. doi: 10.2165/00002018-200730070-00010

    [6]

    ZONGXI Y,JINRONG T,PING Z. Earth science research in US Geological Survey under the big data revolution[J]. Geological Bulletin of China,2013,32(9):1337-1343.

    [7]

    DUNHAM M H. Data mining: Introductory and advanced topics[M]. Pearson Education India, 2006.

    [8]

    SHARMA P,SHARMA S. Past Present & Future of Data Mining[J]. IITM Journal of Information Technology,2018:42.

    [9]

    LI C,YAN W,XIAO K,et al. Analysis and application mode of geological big data[J]. Journal of Geology,2015,39(3):352-357.

    [10]

    LI J. Predictive modelling using random forest and its hybrid methods with geostatistical techniques in marine environmental geosciences[M]//The Proceedings of the Eleventh Australasian Data Mining Conference, 2013.

    [11]

    OBELCZ J, XU K, BENTLEY S J, et al. Sneaky submarine landslides, and how to quantify them: a case study from the Mississippi River Delta front contrasting geophysical and machine learning techniques[C]//AGU Fall Meeting Abstracts, 2017: NH44B-03.

    [12]

    SHENG J,SUN J,BAI Y,et al. Evaluation of hydrocarbon potential using fuzzy AHP-based grey relational analysis:a case study in the Laoshan Uplift,South Yellow Sea,China[J]. Journal of Geophysics Engineering,2020,17(1):189-202. doi: 10.1093/jge/gxz107

    [13]

    刘兰法. 海洋天然气水合物三维地质建模研究[D]. 青岛: 中国石油大学（华东）, 2014.

    [14]

    李连伟,许明明,刘展,等. 天然气水合物数据挖掘服务组件的设计与实现[J]. 计算机应用与软件,2016,33(10):32-36. doi: 10.3969/j.issn.1000-386x.2016.10.008

    [15]

    郑文棠. 岭澳核电三期高边坡三维地形可视化[C]//岩石力学与工程的创新和实践: 第十一次全国岩石力学与工程学术大会, 2010: 5.

    [16]

    DOMBI G W,NANDI P,SAXE J M,et al. Prediction of rib fracture injury outcome by an artificial neural network[J]. Journal of Trauma,1995,39(5):915-921. doi: 10.1097/00005373-199511000-00016

    [17]

    JIANG J L,SU X,ZHANG H,et al. A novel approach to active compounds identification based on support vector regression model and mean impact value[J]. Chemical Biology & Drug Design,2013,81(5):650-657.

    [18]

    LI H,ZHONG Z,LI L,et al. A cascaded QSAR model for efficient prediction of overall power conversion efficiency of all-organic dye-sensitized solar cells[J]. Journal of Computational Chemistry,2015,36(14):1036-1046. doi: 10.1002/jcc.23886

    [19]

    ZHENG J,LAN Q,ZHANG X,et al. Prediction of MRI RF exposure for implantable plate devices using artificial neural network[J]. IEEE Transactions on Electromagnetic Compatibility,2020,62(3):673-681. doi: 10.1109/TEMC.2019.2916837

    [20]

    RUMMEL R J. Applied factor analysis[M]. Northwestern University Press, 1988.

    [21]

    郭晓伟. 基于深度学习的车型识别算法与DSP平台实现[D]. 长沙: 国防科学技术大学, 2016.

    [22]

    李静. 煤矿震动波CT反演探测技术的优化与应用[D]. 徐州: 中国矿业大学, 2017.

    [23]

    RUMELHART D E,HINTON G E,WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature,1986,323(6088):533-536. doi: 10.1038/323533a0

    [24]

    DING S,SU C,YU J. An optimizing BP neural network algorithm based on genetic algorithm[J]. Artificial Intelligence Review,2011,36(2):153-162. doi: 10.1007/s10462-011-9208-z

    [25]

    BOOKER L B,GOLDBERG D E,HOLLAND J H. Classifier systems and genetic algorithms[J]. Artificial Intelligence,1989,40(1-3):235-282. doi: 10.1016/0004-3702(89)90050-7

    [26]

    TARDUNO J A,GEE J. Large-scale motin between Pacific and Atlantic hotspots[J]. Nature,1995,378(6556):477-480. doi: 10.1038/378477a0

    [27]

    朱本铎,吕文超,张伙带. 中西太平洋海山年龄及其地质意义[J]. 矿床地质,2014:1151-1152. doi: 10.3969/j.issn.0258-7106.2014.06.002

    [28]

    TARDUNO J A,DUNCAN R A,SCHOLL D W,et al. The Emperor Seamounts:southward motion of the Hawaiian Hotspot Plume in earth's mantle[J]. Science,2003,301(5636):1064. doi: 10.1126/science.1086442

    [29]

    WATTS A B,SANDWELL D T,SMITH W H F,et al. Global gravity,bathymetry,and the distribution of submarine volcanism through space and time[J]. Journal of Geophysical Research:Solid Earth,2006,111:B08408.

    [30]

    FAGGION O,PINNA E,SAVELLI C,et al. Geomagnetism and age study of Tyrrhenian seamounts[J]. Geophysical Journal International,1995,123(3):915-930. doi: 10.1111/j.1365-246X.1995.tb06898.x

    [31]

    CANDE S C,KENT D V. Ultrahigh resolution marine magnetic anomaly profiles:A record of continuous paleointensity variations?[J]. Journal of Geophysical Research Solid Earth,1992,97(B11):15075-15083. doi: 10.1029/92JB01090

    [32]

    SETON M,MVLLER R D,ZAHIROVIC S,et al. A Global data set of present-day oceanic crustal age and seafloor spreading parameters[J]. Geochemistry,Geophysics,Geosystems,2020,21(10):e2020GC009214.

    [33]

    VOLKER D,GEERSEN J,CONTRERAS-REYES E,et al. Sedimentary fill of the Chile Trench (32–46°S):volumetric distribution and causal factors[J]. Journal of the Geological Society,2013,170(5):723. doi: 10.1144/jgs2012-119

    [34]

    CLAGUE D A,DALRYMPLE G B. Cretaceous K‐Ar ages of volcanic rocks from the Musicians Seamounts and the Hawaiian Ridge[J]. Geophysical Research Letters,2013,2(7):305-308.

    [35]

    HARRIS R N,FISHER A T,CHAPMAN D S. Fluid flow through seamounts and implications for global mass fluxes[J]. Geology,2004,32(8):725-728. doi: 10.1130/G20387.1

    [36]

    SANDWELL D T,MUELLER R D,SMITH W H F,et al. New global marine gravity model from CryoSat-2 and Jason-1 reveals buried tectonic structure[J]. Science,2014,346(6205):65-7. doi: 10.1126/science.1258213

    [37]

    MAUS S,BARCKHAUSEN U,BERKENBOSCH H,et al. EMAG2:a 2–arc min resolution earth magnetic anomaly grid compiled from satellite,airborne,and marine magnetic measurements[J]. Geochemistry Geophysics Geosystems,2009,10(8):Q08005.

    [38]

    MVLLER R D. Age,spreading rates,and spreading asymmetry of the world's ocean crust[J]. Geochemistry Geophysics Geosystems,2008,9(4):Q04006.

    [39]

    STRAUME E O,GAINA C,MEDVEDEV S,et al. GlobSed:updated total sediment thickness in the World's Oceans[J]. Geochemistry Geophysics Geosystems,2019,20(4):1756-1772. doi: 10.1029/2018GC008115

    [40]

    AMANTE C. ETOPO1 1 Arc-Minute Global Relief Model: procedures, data sources and analysis[EB/OL]. http://www.ngdc.noaa.gov/mgg/global/global.html, 2009.

    [41]

    CLOUARD V,BONNEVILLE A. Ages of seamounts,islands,and plateaus on the Pacific plate[J]. Geological Society of America Bulletin,2005:388.

    [42]

    YESSON C,CLARK M R,TAYLOR M L,et al. The global distribution of seamounts based on 30 arc seconds bathymetry data[J]. Deep Sea Research Part I Oceanographic Research Papers,2011,58(4):442-453. doi: 10.1016/j.dsr.2011.02.004

    [43]

    LEE H B,OH J K,PARK C K,et al. Geophysical and sedimentological characteristics of Lomilik Seamount,West Pacific[J]. Ocean & Polar Research,2004,26(2):207-218.

    [44]

    CARBOTTE S M,DIXON J M,FARRAR E,et al. Geological and geophysical characteristics of the Tuzo Wilson Seamounts:implications for plate geometry in the vicinity of the Pacific – North America – Explorer triple junction[J]. Canadian Journal of Earth ences,1989,26(11):2365-2384. doi: 10.1139/e89-202

    [45]

    KIM C H. Magnetic characteristics of TA19-1 and TA19-2 Seamounts in the Lau Basin,the South Western Pacific[J]. Economic and Environmental Geology,2014,47(4):395-404. doi: 10.9719/EEG.2014.47.4.395

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收稿日期:  2022-09-05
录用日期:  2022-11-14
刊出日期:  2023-02-20

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