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    基于时间卷积网络的地震波阻抗反演

    王德涛 陈国雄

    王德涛, 陈国雄, 2022. 基于时间卷积网络的地震波阻抗反演. 地球科学, 47(4): 1492-1506. doi: 10.3799/dqkx.2021.070
    引用本文: 王德涛, 陈国雄, 2022. 基于时间卷积网络的地震波阻抗反演. 地球科学, 47(4): 1492-1506. doi: 10.3799/dqkx.2021.070
    Wang Detao, Chen Guoxiong, 2022. Seismic Wave Impedance Inversion Based on Temporal Convolutional Network. Earth Science, 47(4): 1492-1506. doi: 10.3799/dqkx.2021.070
    Citation: Wang Detao, Chen Guoxiong, 2022. Seismic Wave Impedance Inversion Based on Temporal Convolutional Network. Earth Science, 47(4): 1492-1506. doi: 10.3799/dqkx.2021.070

    基于时间卷积网络的地震波阻抗反演

    doi: 10.3799/dqkx.2021.070
    基金项目: 

    国家自然科学基金面上基金 41972305

    详细信息
      作者简介:

      王德涛(1996-),男,硕士研究生,主要从事人工智能地震解释研究工作. ORCID:0000-0002-9349-2707. E-mail:detao_wang@126.com

      通讯作者:

      陈国雄,E-mail:gxchen@cug.edu.cn

    • 中图分类号: P315

    Seismic Wave Impedance Inversion Based on Temporal Convolutional Network

    • 摘要: 近些年来,深度学习网络的兴起极大地推动了人工智能技术在地震数据处理、反演以及解译等领域的应用.地震波阻抗反演是石油地震勘探领域的一项关键技术,其反演精度在圈定油气储层构造中起到非常重要的作用.提出了一种基于数据驱动时间卷积网络(temporal convolution network,TCN)模型的地震波阻抗反演方法,旨在无需建立初始反演模型,直接利用工区的少量测井标签数据,以地震振幅数据为输入,将波阻抗反演转化为时间序列建模任务,最终输出地下模型的阻抗信息.采用Marmousi2数据集对基于TCN的波阻抗反演模型进行训练、验证和测试,结果显示,在测试集上该模型预测结果的皮尔逊系数和决定系数分别达到97.92%和95.95%,并对远离训练区域的波阻抗信息预测有着良好的泛化性,且在预测时间和预测精度等方面都要明显优于前人的相关研究工作.上述结果表明,TCN时间序列深度学习模型在复杂地层波阻抗反演中具有一定优越性和应用前景,为地震波阻抗反演提供了新思路.

       

    • 图  1  时间卷积网络结构图

      x0x1,…,xt为的时序输入,yt是网络预测值,d为扩张卷积中的膨胀系数

      Fig.  1.  Network structure of TCN model

      图  2  因果卷积

      Fig.  2.  Causal convolution

      图  3  多层扩张-因果卷积堆叠

      d为扩张卷积中的膨胀系数

      Fig.  3.  Multi dilated-causal convolution layers stack

      图  4  残差块示意图

      Fig.  4.  The structure of a residual block

      图  5  网络模型结构图

      a.多层扩张-非因果卷积堆叠;b.残差块;c.TCN模型结构图,其中filters代表卷积核的数量,k代表卷积核的大小

      Fig.  5.  Structure of network model

      图  6  模型算法流程图

      Fig.  6.  Flow chart of model algorithm

      图  7  Marmousi-2模型示意图

      a.代表叠后地震剖面;b.为波阻抗剖面

      Fig.  7.  Marmousi-2 model diagram

      图  8  数据预处理

      a.原始地震记录;b.地震数据重采样;c.标准归一化

      Fig.  8.  Data preprocessing

      图  9  Bi-CLSTM网络结构示意图

      Fig.  9.  The architecture of Bi-CLSTM network schematic diagram

      图  10  不同模型的训练过程

      图a和c分别代表方案1中Re-TCN、Bi-CLSTM的训练过程;图b和d分别代表方案2中TCN、Re-Bi-CLSTM的训练过程

      Fig.  10.  Training process of the various network models

      图  11  单道预测结果图

      图a~d分别代表Re-TCN、TCN、Bi-CLSTM、Re-Bi-CLSTM的预测结果,其中黑线代表实际波阻抗数据、红线为预测值

      Fig.  11.  Graphs of single trace prediction results

      图  12  波阻抗剖面预测结果

      Fig.  12.  Prediction results of wave impedance section

      图  13  绝对误差

      预测波阻抗剖面与真实值的差值

      Fig.  13.  Absolute difference graphs

      图  14  预测值和真实波阻抗的散点图

      图a~d分别代表Re-TCN,TCN,Bi-CLSTM,Re-Bi-CLSTM预测波阻抗和真实值的散点示意图

      Fig.  14.  Scatter plots of the estimated and true AI

      图  15  波阻抗剖面预测结果

      红色虚线之间代表训练区域,之外表示预测区域

      Fig.  15.  Prediction results of wave impedance section

      图  16  单道预测结果

      trace=0、320、2 260、2 580为远离训练区域的波阻抗数据

      Fig.  16.  Graphs of single trace prediction results

      表  1  波阻抗反演的评价指标

      Table  1.   Evaluation index of wave impedance inversion

      方案 模型 预测时间(s) 训练 测试
      PCC r2 PCC r2
      方案1 Re-TCN 10 0.984 9 0.970 2 0.971 1 0.943 8
      Bi-CLSTM 119 0.961 4 0.924 7 0.955 9 0.914 4
      方案2 TCN 9 0.994 4 0.988 8 0.979 2 0.959 5
      Re-Bi-CLSTM 274 0.923 5 0.853 8 0.907 9 0.827 1
      下载: 导出CSV
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    • 收稿日期:  2021-02-27
    • 刊出日期:  2022-04-25

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