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    基于深度学习的地质找矿大数据挖掘与集成的挑战

    左仁广 彭勇 李童 熊义辉

    左仁广, 彭勇, 李童, 熊义辉, 2021. 基于深度学习的地质找矿大数据挖掘与集成的挑战. 地球科学, 46(1): 350-358. doi: 10.3799/dqkx.2020.111
    引用本文: 左仁广, 彭勇, 李童, 熊义辉, 2021. 基于深度学习的地质找矿大数据挖掘与集成的挑战. 地球科学, 46(1): 350-358. doi: 10.3799/dqkx.2020.111
    Zuo Renguang, Peng Yong, Li Tong, Xiong Yihui, 2021. Challenges of Geological Prospecting Big Data Mining and Integration Using Deep Learning Algorithms. Earth Science, 46(1): 350-358. doi: 10.3799/dqkx.2020.111
    Citation: Zuo Renguang, Peng Yong, Li Tong, Xiong Yihui, 2021. Challenges of Geological Prospecting Big Data Mining and Integration Using Deep Learning Algorithms. Earth Science, 46(1): 350-358. doi: 10.3799/dqkx.2020.111

    基于深度学习的地质找矿大数据挖掘与集成的挑战

    doi: 10.3799/dqkx.2020.111
    基金项目: 

    国家自然科学项目 41772344

    详细信息
      作者简介:

      左仁广(1981-), 男, 教授, 主要从事数学地质与矿产勘查方面的研究.ORCID:0000-0002-5639-3128.E-mail:zrguang@cug.edu.cn

    • 中图分类号: P577

    Challenges of Geological Prospecting Big Data Mining and Integration Using Deep Learning Algorithms

    • 摘要: 基于深度学习的地质找矿信息挖掘与集成已经成为数学地球科学的前沿领域.深度学习作为一种具有多级非线性变换的层级机器学习算法,在地质找矿大数据挖掘与集成中仍处于探索阶段,还有一系列问题亟需解决.以卷积神经网络为例,探讨了基于深度学习的地质找矿大数据挖掘与集成过程中两大挑战:训练样本不足和深度学习网络模型构建困难,重点分析了基于复制和添加噪声的地质找矿数据增强技术并开展了多组对比实验,构建了适用于地质找矿大数据挖掘与集成的训练样本和卷积神经网络模型.该模型对闽西南铁多金属成矿区的地质、地球物理和地球化学等多源数据进行了特征提取与集成融合,圈定了找矿远景区,为该区进一步找矿提供了科学依据.

       

    • 图  1  数据增强方法

      Fig.  1.  Data augmentation methods

      图  2  复制与添加噪声的数据增强方法

      Fig.  2.  Data augmentation methods: duplication and adding noises

      图  3  滑动窗口制作样本示意图

      Fig.  3.  The sliding window method for making samples

      图  4  研究区地质简图

      Fig.  4.  Simplified geological map of the study area Xiong et al.(2018)

      图  5  训练样本空间尺寸对CNN模型预测结果的影响

      Fig.  5.  Effects of sliding window sizes on the performance of CNN

      图  6  数据增强方法对CNN模型预测结果的影响

      a.复制;b.添加噪声(200倍);c.添加噪声(500倍);d.混合样本

      Fig.  6.  Effects of different data augmentation methods on the performance of CNN

      图  7  卷积层数目对CNN模型预测结果的影响

      a. 2层;b. 3层;c. 4层;d. 5层

      Fig.  7.  Effects of different number of convolutional layers on the performance of CNN with different convolutional layers

      图  8  全连接层数目对CNN模型预测结果的影响

      Fig.  8.  Effects of different numbers of fully connection layer on the performance of CNN with different fully connection layers

      图  9  CNN模型结构

      Fig.  9.  The structure of convolutional neural network model

      图  10  CNN预测结果

      Fig.  10.  Predictive map via CNN

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    • 收稿日期:  2020-04-03
    • 刊出日期:  2021-01-15

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