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基于深度学习的大地电磁二维反演方法

王方 熊杰 田慧潇 李思平 康佳帅

王方, 熊杰, 田慧潇, 李思平, 康佳帅. 基于深度学习的大地电磁二维反演方法[J]. 地质科技通报, 2024, 43(2): 344-354. doi: 10.19509/j.cnki.dzkq.tb20220471
引用本文: 王方, 熊杰, 田慧潇, 李思平, 康佳帅. 基于深度学习的大地电磁二维反演方法[J]. 地质科技通报, 2024, 43(2): 344-354. doi: 10.19509/j.cnki.dzkq.tb20220471
WANG Fang, XIONG Jie, TIAN Huixiao, LI Siping, KANG Jiashuai. Two-dimensional magnetotelluric inversion method based on deep learning[J]. Bulletin of Geological Science and Technology, 2024, 43(2): 344-354. doi: 10.19509/j.cnki.dzkq.tb20220471
Citation: WANG Fang, XIONG Jie, TIAN Huixiao, LI Siping, KANG Jiashuai. Two-dimensional magnetotelluric inversion method based on deep learning[J]. Bulletin of Geological Science and Technology, 2024, 43(2): 344-354. doi: 10.19509/j.cnki.dzkq.tb20220471

基于深度学习的大地电磁二维反演方法

doi: 10.19509/j.cnki.dzkq.tb20220471
基金项目: 

国家自然科学基金项目 62273060

国家自然科学基金项目 61673006

长江大学大学生创新创业项目 Yz2022055

详细信息
    作者简介:

    王方, E-mail: 202072661@yangtzeu.edu.cn

    通讯作者:

    熊杰, E-mail: xiongjie@yangtzeu.edu.cn

  • 中图分类号: P631.3+25

Two-dimensional magnetotelluric inversion method based on deep learning

More Information
  • 摘要:

    如何通过大地电磁测深反演方法来提高数据解释的精度一直都是大地电磁测深研究领域的重要课题。针对大地电磁传统反演方法存在的初始模型依赖、易陷入局部最优的问题,提出了一种基于深度学习的大地电磁二维反演方法。该方法首先设计GoogLeNetINV神经网络;接着构造多种地电模型,在TM模式下通过正演得到视电阻率数据,组成训练数据集;然后用训练数据集训练该神经网络并调整网络参数;最后,将视电阻率数据输入已训练好的GoogLeNetINV神经网络直接得到反演结果。实验结果表明,该方法能快速、准确地反演出“未学习”过地电模型的位置和电阻率数据,具有较好的泛化能力;使用噪声数据测试仍能取得良好的反演结果,有一定的抗噪声能力。将该神经网络应用于Bendigo Zone实际数据资料处理中,反演得到的电阻率模型与地震解释一致,因此基于深度学习的大地电磁反演方法能有效解决大地电磁反演问题。

     

  • 图 1  基于深度学习的大地电磁反演过程(①~④为反演步骤)

    Figure 1.  Magnetotelluric inversion process based on deep learning

    图 2  Inception结构

    Figure 2.  Inception structure

    图 3  GoogLeNetINV神经网络结构

    Figure 3.  GoogLeNetINV neural network structure

    图 4  正演观测系统和模型几何设计

    a.观测系统设计;b.模型几何设计

    Figure 4.  Geometric design of the forward observation system and model

    图 5  部分验证集样本反演结果

    a. 6×6(3 000 Ω·m);b. 5×10(200 Ω·m);c. 3层4×8(2 500 Ω·m);d. 8×4(200 Ω·m);e. 5×10(2 500 Ω·m);f. 6×6(500 Ω·m)

    Figure 5.  Inversion results for the partial validation set samples

    图 6  实验1反演结果

    a. 10×5组合模型(500 Ω·m);b. 10×5与3层4×8组合模型(3 000 Ω·m); c. 3层4×8组合模型(3 000 Ω·m)

    Figure 6.  Inversion results of experiment one

    图 7  实验2反演结果

    a. 7×7(2 300 Ω·m); b. 4层4×8(700 Ω·m)

    Figure 7.  Inversion results of experiment two

    图 8  加入0%,3%和5%高斯白噪声的视电阻率

    a.未加噪声的视电阻率; b.加入3%高斯白噪声的视电阻率; c.加入5%高斯白噪声的视电阻率

    Figure 8.  Apparent resistivity with 0%, 3% and 5% white Gaussian noise

    图 9  加入高斯白噪声的反演结果

    a. 4层4×8(200 Ω·m)3%高斯白噪声;b. 6×12(2 500 Ω·m)3%高斯白噪声; c. 7×7(3 000 Ω·m)3%高斯白噪声; d. 4层4×8(200 Ω·m)5%高斯白噪声; e. 6×12(2 500 Ω·m)5%高斯白噪声;f. 7×7(3 000 Ω·m)5%高斯白噪声

    Figure 9.  Inversion results with white Gaussian noise

    图 10  卷积神经网络(CNN)反演结果[14]

    a.低阻异常浅部反演; b. 低阻异常中部反演; c.低阻异常深部反演; d. 地堑浅部反演; e. 地堑中部反演; f.地堑深部反演

    Figure 10.  Inversion results of the convolutional neural network (CNN)

    图 11  GoogLeNetINV神经网络反演结果

    a.低阻异常浅部反演; b.低阻异常中部反演; c.低阻异常深部反演; d. 地堑浅部反演; e. 地堑中部反演; f.地堑深部反演

    Figure 11.  Inversion results of the GoogLeNetINV neural network

    图 12  区域地质图(据文献[22]修改)

    RR为远程参考站点;MT测深用黄色圆圈表示,圆圈上数字代表测点编号;Cayley等[23]于2011年绘制的MT地震剖面线06GAV-02和06GAV-03用蓝色粗线表示。地质走向显示在图左下方,主要走向近南北向,约N5°W,次要方向为北东向,约为N40°E。AVF、CTF、MFF、WLF、HFZ、MWF均为断层代号,图 14

    Figure 12.  Regional geological map

    图 13  实际数据反演结果对比

    a. GoogLeNetINV神经网络反演; b. NLCG代码反演结果[22],▽为取样点

    Figure 13.  Comparison of inversion results for field data

    图 14  地震偏移剖面及其解释[24]

    a. 地震偏移剖面(06GAV-02); b. 地震解释

    Figure 14.  Seismic migrated section and its interpretation

    表  1  GoogLeNetINV神经网络反演参数设置

    Table  1.   GoogLeNetINV neural network inversion parameter settings

    分类 参数设置 GoogLeNetINV神经网络
    数据集 训练集 7 190
    验证集 1 797
    网络设置 学习率 η=0.001
    激活函数 ReLu
    优化器 Adam
    L2正则化 λ=0.003
    Dropout 0.5
    训练过程 epochs 3 000
    batch size 300
    下载: 导出CSV

    表  2  GoogLeNetINV神经网络反演及预测所需时间

    Table  2.   Training and prediction times of the GoogLeNetINV neural network

    训练时间/s 3 000
    预测时间/s 1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-08-25
  • 录用日期:  2022-11-30
  • 修回日期:  2022-11-16

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