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    基于半监督神经网络的铜矿预测方法

    徐永洋 李孜轩 谢忠 冯斌 陈浩

    徐永洋, 李孜轩, 谢忠, 冯斌, 陈浩, 2020. 基于半监督神经网络的铜矿预测方法. 地球科学, 45(12): 4563-4573. doi: 10.3799/dqkx.2020.297
    引用本文: 徐永洋, 李孜轩, 谢忠, 冯斌, 陈浩, 2020. 基于半监督神经网络的铜矿预测方法. 地球科学, 45(12): 4563-4573. doi: 10.3799/dqkx.2020.297
    Xu Yongyang, Li Zixuan, Xie Zhong, Feng Bin, Chen Hao, 2020. Prediction of Copper Mineralization Based on Semi-Supervised Neural Network. Earth Science, 45(12): 4563-4573. doi: 10.3799/dqkx.2020.297
    Citation: Xu Yongyang, Li Zixuan, Xie Zhong, Feng Bin, Chen Hao, 2020. Prediction of Copper Mineralization Based on Semi-Supervised Neural Network. Earth Science, 45(12): 4563-4573. doi: 10.3799/dqkx.2020.297

    基于半监督神经网络的铜矿预测方法

    doi: 10.3799/dqkx.2020.297
    基金项目: 

    国家自然科学基金 41671400

    国家重点研发计划项目 2018YFB0505500

    国家重点研发计划项目 2018YFB0505504

    开放基金 CY119R015

    地质探测与评估教育部重点实验室开放基金 GLAB2020ZR05

    详细信息
      作者简介:

      徐永洋(1989-), 男, 副教授, 主要从事智能空间认知方面的研究.ORCID:0000-0001-7421-4915.E-mail:yongyangxu@cug.edu.cn

      通讯作者:

      谢忠, E-mail:xiezhong@cug.edu.cn

    • 中图分类号: P3

    Prediction of Copper Mineralization Based on Semi-Supervised Neural Network

    • 摘要: 将人工智能技术引入成矿预测研究中,可以提高预测效率,挖掘探测数据与结果之间的隐藏信息.利用半监督学习方法对样本构建要求低的优点,结合其在异常识别方面的应用效果,设计了基于分割准则的孤立森林与深度自编码网络的神经网络结构;基于西藏冈底斯地区的化探元素数据,对研究区内的铜矿进行了成矿预测工作,预测结果与已知矿区数据叠加效果较好,说明本文的神经网络结构能够完成成矿远景区的预测工作.

       

    • 图  1  基于非监督学习成矿预测流程图

      Fig.  1.  Flow chart of metallogenic prediction based on unsupervised learning method

      图  2  自编码器神经网络结构

      Fig.  2.  Network structure of Autoencoder

      图  3  生成对抗网络机制的自编码器网络结构

      Fig.  3.  Structure of encoder-decoder GAN

      图  4  SCiForest预测结果的预测值分布

      Fig.  4.  Value distribution of prediction results by SCiForest

      图  5  基于SCiForest预测值小于0.4的数据训练的自编码器的迭代训练损失

      Fig.  5.  Iterative training loss of Autoencoder based on data training with SCiForest prediction value less than 0.4

      图  6  自编码器生成对抗网络的重构误差分布

      Fig.  6.  Reconstruction error distribution of the encoder-decoder GAN

      图  7  基于SCiForest预测值小于0.4的数据训练的半监督神经网络预测结果的插值可视化

      a.未插值优化;b.插值优化

      Fig.  7.  Interpolation visualization of prediction results of semi-supervised neural network based on data training with SCiForest prediction value less than 0.4

      图  8  预测结果叠加矿区示意

      a.基于SCiForest预测值小于0.3的数据训练的半监督神经网络预测结果的插值可视化;b.SCiForest预测值小于0.4;c.SCiForest预测值小于0.5

      Fig.  8.  Schematic diagrams of prediction results superimposed mining area

      图  9  基于DBSCAN的半监督神经网络预测结果的插值可视化

      Fig.  9.  Interpolation visualization of prediction results of semi-supervised neural network based on DBSCAN

      图  10  预测出的潜在矿区示意

      Fig.  10.  Predicted potential mining area

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    • 收稿日期:  2020-07-20
    • 刊出日期:  2020-12-15

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