基于深度卷积神经网络的地震数据随机噪声压制

陈天 易远元

陈天,易远元. 2021. 基于深度卷积神经网络的地震数据随机噪声压制. 地震学报,43(4):474−482 doi: 10.11939/jass.20200135
引用本文: 陈天,易远元. 2021. 基于深度卷积神经网络的地震数据随机噪声压制. 地震学报,43(4):474−482 doi: 10.11939/jass.20200135
Chen T,Yi Y Y. 2021. Random noise suppression of seismic data based on deep convolution neural network. Acta Seismologica Sinica,43(4):474−482 doi: 10.11939/jass.20200135
Citation: Chen T,Yi Y Y. 2021. Random noise suppression of seismic data based on deep convolution neural network. Acta Seismologica Sinica43(4):474−482 doi: 10.11939/jass.20200135

基于深度卷积神经网络的地震数据随机噪声压制

doi: 10.11939/jass.20200135
基金项目: 华北石油第三轮校企合作项目(HBYT-YJY-2018-JS-507)资助
详细信息
    通讯作者:

    易远元,e-mail:ctlovezrs@163.com

  • 中图分类号: P315.69

Random noise suppression of seismic data based on deep convolution neural network

  • 摘要: 本文以提高地震数据的成像质量为目标,提出一种智能的卷积神经网络降噪框架,从带有噪声的地震数据中自适应地学习地震信号。为了加速网络训练和避免训练时出现梯度消失现象,我们在网络中加入残差学习和批标准化的方法,并采用了ReLU激活函数和Adam优化算法优化网络。此外,Marmousi和F3数据集被用来对网络进行训练和测试,经过充分训练的网络不仅能在学习中保留地震数据特征,而且能去除随机噪声。首先充分地训练网络,从中提取出随机噪声,并保留学习到的地震数据特征,之后通过重建地震数据估算测试集中的波形特征。合成记录和实际数据的处理结果显示了深度卷积神经网络在随机噪声压制任务中的潜力,并通过实验验证表明了深度卷积神经网络框架有很好的去噪效果。

     

  • 图  1  深层卷积神经网络(DCNN)框架的结构

    Figure  1.  Structure of deep convolutional neural network framework

    图  2  合成数据的去噪结果(图中方柱表示有效信号,下同)

    (a) 干净数据;(b) 加噪数据;(c) NLM去噪结果;(d) BM3D去噪结果;(e) DCNN去噪结果;(f) NLM去噪残差剖面;(g) BM3D去噪残差剖面;(h) DCNN去噪残差剖面

    Figure  2.  Denoising results of synthetic data (Valid signals are in the boxes,the same below)

    (a) Clean data;(b) Noisy data;(c) NLM denoising results;(d) BM3D denoising results;(e) DCNN denoising results;(f) NLM denoising residual profile;(g) BM3D denoising residual profile;(h) DCNN denoising residual profile

    图  3  F3叠后数据的去噪结果

    (a) 干净数据;(b) 加噪数据(PSNR=20.4 dB);(c) NLM去噪结果(PSNR=28.97 dB,SSIM=0.868 9);(d) BM3D去噪结果(PSNR=30.31 dB,SSIM=0.942 8);(e) DCNN去噪结果(PSNR=32.43 dB,SSIM=0.965 8);(f) NLM去噪残差剖面;(g) BM3D去噪残差剖面;(h) DCNN去噪残差剖面

    Figure  3.  Denoising results of post-stack synthetic data for F3

    (a) Clean data;(b) Noisy data (PSNR=20.4 dB);(c) NLM denoising results (PSNR=28.89 dB,SSIM=0.867 2);(d) BM3D denoising results (PSNR=30.34 dB, SSIM=0.943 6);(e) DCNN denoising results (PSNR=32.41 dB,SSIM=0.966 6); (f) NLM denoising residual profile;(g) BM3D denoising residual profile;(h) DCNN denoising residual profile

    3  F3叠后数据的去噪结果

    (a) 干净数据;(b) 加噪数据(PSNR=20.4 dB);(c) NLM去噪结果(PSNR=28.97 dB,SSIM=0.868 9);(d) BM3D去噪结果(PSNR=30.31 dB,SSIM=0.942 8);(e) DCNN去噪结果(PSNR=32.43 dB,SSIM=0.965 8);(f) NLM去噪残差剖面;(g) BM3D去噪残差剖面;(h) DCNN去噪残差剖面

    3.  Denoising results of post-stack synthetic data for F3

    (a) Clean data;(b) Noisy data (PSNR=20.4 dB);(c) NLM denoising results (PSNR=28.89 dB,SSIM=0.867 2);(d) BM3D denoising results (PSNR=30.34 dB, SSIM=0.943 6);(e) DCNN denoising results (PSNR=32.41 dB,SSIM=0.966 6); (f) NLM denoising residual profile;(g) BM3D denoising residual profile;(h) DCNN denoising residual profile

    表  1  不同去噪方法的评价指标对比(合成数据)

    Table  1.   Comparison of evaluation indicators of different denoising methods (synthetic data)

    噪声水平/dB输入信号/dB去噪算法峰值信噪比/dB结构相似性指数
    10 28.13 NLM 34.64 0.893 7
    BM3D 34.78 0.942 9
    DCNN 35.12 0.959 3
    15 24.59 NLM 31.74 0.850 2
    BM3D 32.85 0.932 1
    DCNN 34.16 0.951 7
    20 22.09 NLM 30.94 0.795 1
    BM3D 31.61 0.901 5
    DCNN 32.78 0.938 2
    25 20.15 NLM 29.56 0.742 4
    BM3D 30.43 0.894 1
    DCNN 31.54 0.927 8
    30 18.61 NLM 28.53 0.696 7
    BM3D 29.24 0.874 9
    DCNN 30.19 0.906 3
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出版历程
  • 收稿日期:  2020-08-07
  • 修回日期:  2020-11-19
  • 网络出版日期:  2021-08-16
  • 刊出日期:  2021-07-15

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