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    基于BiX-NAS的地震层序智能识别——以荷兰近海地区F3数据为例

    陈建玮 陈国雄 王德涛 徐富文

    陈建玮, 陈国雄, 王德涛, 徐富文, 2023. 基于BiX-NAS的地震层序智能识别——以荷兰近海地区F3数据为例. 地球科学, 48(8): 3162-3178. doi: 10.3799/dqkx.2023.014
    引用本文: 陈建玮, 陈国雄, 王德涛, 徐富文, 2023. 基于BiX-NAS的地震层序智能识别——以荷兰近海地区F3数据为例. 地球科学, 48(8): 3162-3178. doi: 10.3799/dqkx.2023.014
    Chen Jianwei, Chen Guoxiong, Wang Detao, Xu Fuwen, 2023. Intelligent Seismic Stratigraphic Identification Based on BiX-NAS: A Case Study from the F3 Dataset in Netherlands Offshore Area. Earth Science, 48(8): 3162-3178. doi: 10.3799/dqkx.2023.014
    Citation: Chen Jianwei, Chen Guoxiong, Wang Detao, Xu Fuwen, 2023. Intelligent Seismic Stratigraphic Identification Based on BiX-NAS: A Case Study from the F3 Dataset in Netherlands Offshore Area. Earth Science, 48(8): 3162-3178. doi: 10.3799/dqkx.2023.014

    基于BiX-NAS的地震层序智能识别——以荷兰近海地区F3数据为例

    doi: 10.3799/dqkx.2023.014
    基金项目: 

    国家自然科学基金面上项目 41972305

    原创探索计划项目 42050103

    地质过程与矿产资源国家重点实验室科技部专项经费资助 MSFGPMR2022-3

    详细信息
      作者简介:

      陈建玮(1999-), 男, 硕士研究生, 主要从事人工智能地震解释研究.ORCID: 0000-0001-7953-7836.E-mail: chenjw@cug.edu.cn

      通讯作者:

      陈国雄, ORCID: 0000-0002-6785-9675.E-mail: gxchen@cug.edu.cn

    • 中图分类号: P628

    Intelligent Seismic Stratigraphic Identification Based on BiX-NAS: A Case Study from the F3 Dataset in Netherlands Offshore Area

    • 摘要: 近些年来,深度学习方法在地震数据处理和解释领域得到了广泛关注和应用,其中大多数深度学习算法采用了端到端的深度卷积神经网络以实现地质体特征的提取与识别(如地层、断裂以及盐丘等).然而,这些算法往往含有数十万甚至百万的可训练参数,导致模型存在参数冗余、训练效率低等问题.为了解决上述问题,构建了一个轻量化的双向多尺度网络结构模型用于地震层序智能识别.该模型通过两阶段神经网络体系结构搜索算法(neural architecture search,NAS)剔除了双向多尺度网络结构的冗余连接,使得网络结构大幅简化,从而减少参数冗余,进而提高训练效率.采用荷兰近海地区的F3地震数据集对基于NAS算法简化的双向多尺度网络结构地层识别模型进行训练、验证和预测.结果表明:在实际的地层识别任务中,该轻量化模型的平均识别准确率达到了95.52%,并对远离训练工区的预测集具有良好的泛化性.此外,该模型的参数量仅为U形卷积神经网络(U-Net)模型的4.4%,在训练效率、模型参数量等方面优于前人的相关研究工作;并对地震振幅中的噪声干扰具有鲁棒性.因此,这些结果展现了BiX-NAS网络模型在实际地震地层自动识别中良好的应用前景.

       

    • 图  1  BiO-Net网络模型结构图

      Fig.  1.  Structure of BiO-Net model

      图  2  解码块内部结构图

      Fig.  2.  Internal structure of Decoder Block

      图  3  编码块内部结构图

      Fig.  3.  Internal structure of Encoder Block

      图  4  迭代次数为2、网络层数为4的BiO-Net++网络结构图

      Fig.  4.  Structure of BiO-Net++ with 4 levels and 2 iterations

      图  5  BiO-Net++(N=5, L=4)的选择矩阵的两种情况示意图

      Fig.  5.  Two cases of selection matrix, BiO-Net++(N=5, L=4)

      图  6  提取阶段示意图

      Fig.  6.  Extraction stage

      图  7  头部网络、尾部网络结构图

      Fig.  7.  Structure of head network and tail network

      图  8  BiX-NAS网络模型结构图

      Fig.  8.  Structure of BiX-NAS model

      图  9  基于BiX-NAS的地层识别算法流程示意图

      Fig.  9.  Stratigraphic identification flow diagram based on BiX-NAS

      图  10  地震剖面及标签数据图

      Fig.  10.  Picture of seismic profile and label data

      图  11  联络测线数据预处理流程示意图

      Fig.  11.  Flow diagram of crossline data preprocessing

      图  12  BiX-NAS模型与U-Net模型不同轮数下的训练损失

      Fig.  12.  Training loss of BiX-NAS model and U-Net model at different epochs

      图  13  验证集不同模型识别结果对比图

      a. 标签数据;b. U-Net模型识别结果;c. BiX-NAS模型识别结果

      Fig.  13.  Comparison of identification results of different modelson validation set

      图  14  预测集不同模型识别结果对比图

      a.U-Net模型识别结果;b. BiX-NAS模型识别结果

      Fig.  14.  Comparison of identification results of different models on prediction set

      图  15  三维地层识别示意图

      Fig.  15.  The illustration of 3D stratum identification

      图  16  三维地层识别剖面示意图

      a.地震剖面振幅数据(Time=1 360 ms);b.BiX-NAS模型识别结果(Time=1 360 ms);c.地震剖面振幅数据(Time=1 600 ms);d. BiX-NAS模型识别结果(Time=1 600 ms)

      Fig.  16.  The profilemap of 3D stratum identification

      图  17  BiX-NAS模型与U-Net模型不同高斯噪声下的预测集精度

      Fig.  17.  Prediction set MIoU of BiX-NAS model and U-Net model in the different Gaussian noise environment

      图  18  联络测线切片#1025不同噪声和不同模型下的地层识别结果

      a. 标签数据;b.U-Net模型不同高斯噪声(σ=10~50)下的识别结果(i);c. BiX-NAS模型不同高斯噪声(σ=10~50)下的识别结果(i);d. U-Net模型不同高斯噪声(σ=10~50)下的识别结果(ii);e. BiX-NAS模型不同高斯噪声(σ=10~50)下的识别结果(ii)

      Fig.  18.  Stratum identification results of crossline #1025 in the different Gaussian noise environment and models

      算法1:NAS搜索算法:
          输入:网络迭代次数T;各相邻提取阶段所有跳跃连接组合的集合C;头部网络结构H;各相邻提取阶段的最优网络保留数S;网络结构评价标准Rank
          #C={C(1), C(2), ..., C(t), ..., C(2T-1)},C(t)表示第t个提取阶段与第t+1个提取阶段间的所有跳跃连接组合的集合;
          #C(t)={C1(t), C2(t), ..., Ci(t), ...},Ci(t)表示第t个提取阶段与第t+1个提取阶段间的第i种跳跃连接组合,ntC(t)的基数;
          #H(1)→(t)表示第1个提取阶段到第t个提取阶段的头部网络结构;
          #S={s1→2, s1→2, ..., st→(t+1), ..., s(2T-1)→2T},st→(t+1), 表示第t个提取阶段到第t+1个提取阶段的最优网络结构保留数;
          #Rank表示网络评价标准;
          输出:m个提取阶段至第2T个提取阶段间的最简跳跃连接组合的集合E(m)→(2T),其中m∈[1, 2T-1].
          #E(m)→(2T)={E1(m)→(2T), E2(m)→(2T), ...,Ei(m)→(2T), ...,$ {E}_{{s}_{m}\to (m+1)}^{\left(m\right)\to \left(2T\right)} $},Ei(m)→(2T)表示第m个提取阶段至第2T个提取阶段间的第i种最简跳跃连接组合;
      1: for t=2T-1, …, 1 do
      2:    if t=2T-1 then
      3:        for j=1, …, nt do
      4:    Forward head network H(1)→(t)
      5:    Forward tail network with sampled skips cj(t)
      6:    Rank (H(1)→(t), cj(t))
      7:    end for
      8:    E(2T-1)→(2T)←Optimal solution of Rank (H(1)→(t), cj(t))
      9: else
      10:    for i=1, …, s(t+1)→(t+2) do
      11:      Forward head network H(1)→(t)
      12:      for j=1, …, nt do
      13:        Forward tail network with sampled skips cj(t), Ei(t+1)→(2T)
      14:        Rank (H(1)→(t), cj(t), Ei(t+1)→(2T))
      15:      end for
      16:    end for
      17:    H(t)→(2T)←Optimal solution of Rank (H(1)→(t), cj(t), Ei(t+1)→(2T))
      18:  end if
      19: end for
      下载: 导出CSV

      表  1  不同层位的地震相特征解释

      Table  1.   Interpretation of seismic facies characteristics in different horizons

      层位序号 地震相特征解释
      1 顶部反射具有分层的地震相,下部为具有均匀的地震相,无明显的反射
      2 该层的反射信号主要由低振幅且连续的反射信号组成
      3 该层的反射信号主要呈现不连续的丘状特征
      4 该层的反射信号主要呈现为亚平行态
      5 该层的反射信号主要呈现为中低幅度的S型曲线结构
      6 该层的反射信号主要由平行的高振幅反射信号组成
      7 该层的反射信号主要由半连续和低振幅的反射信号组成
      8 该层的反射信号主要呈现为扭曲状和低振幅相
      9 该层内没有明显反射信号,由低振幅组成
      下载: 导出CSV

      表  2  不同迭代次数的BiO-Net网络模型在不同数据集上的训练结果

      Table  2.   Training results of BiO-Net with different iterations on different data sets

      NO. Dataset Model Epochs Params
      (×106)
      MACs
      (×109)
      Val_MIoU
      #1 #1 BiO-Net(iter=1) 100 14.964 1 11.300 2 0.967 5
      #2 #2 BiO-Net(iter=1) 100 14.964 1 11.300 2 0.957 0
      #3 #1 BiO-Net(iter=2) 100 14.980 1 36.427 1 0.962 9
      #4 #2 BiO-Net(iter=2) 100 14.980 1 36.427 1 0.953 7
      #5 #1 BiO-Net(iter=3) 100 14.996 0 77.277 2 0.942 0
      #6 #2 BiO-Net(iter=3) 100 14.996 0 77.277 2 0.947 5
      #7 #1 BiO-Net(iter=4) 100 15.011 9 133.850 3 0.794 2
      #8 #2 BiO-Net(iter=4) 100 15.011 9 133.850 3 0.934 6
      #9 #1 BiO-Net(iter=5) 100 15.027 9 206.146 6 0.324 0
      #10 #2 BiO-Net(iter=5) 100 15.027 9 206.146 6 0.795 1
      下载: 导出CSV

      表  3  不同迭代次数的Phase1-Searched-Net网络模型在不同数据集上的训练结果

      Table  3.   Training results of Phase1-Searched-Net with different iterations on different data sets

      NO. Dataset Model Epochs Params
      (×106)
      MACs
      (×109)
      Val_MIoU
      #11 #1 Phase1-Searched-Net(iter=1) 100 0.422 1 6.735 0 0.954 7
      #12 #2 Phase1-Searched-Net(iter=1) 100 0.422 1 6.735 0 0.946 2
      #13 #1 Phase1-Searched-Net(iter=2) 100 0.423 6 50.142 7 0.953 1
      #14 #2 Phase1-Searched-Net(iter=2) 100 0.423 6 49.799 5 0.943 3
      #15 #1 Phase1-Searched-Net(iter=3) 100 0.425 1 151.297 7 0.951 8
      #16 #2 Phase1-Searched-Net(iter=3) 100 0.425 1 124.930 6 0.944 1
      下载: 导出CSV

      表  4  BiX-NAS网络模型在不同数据集上的训练结果

      Table  4.   Training results of BiX-NASon different data sets

      NO. Dataset Model Epochs Rank Re_Epochs Params
      (×106)
      MACs
      (×109)
      Val_MIoU
      #17 #1 BiX-NAS 10 #1 100 0.380 5 5.369 6 0.950 5
      #18 #1 BiX-NAS 10 #2 100 0.339 1 5.027 2 0.955 2
      #19 #2 BiX-NAS 10 #1 100 0.380 5 5.370 9 0.945 9
      #20 #2 BiX-NAS 10 #2 100 0.380 5 5.370 9 0.943 7
      #21 #1 BiX-NAS 20 #1 100 0.380 5 5.371 6 0.947 6
      #22 #1 BiX-NAS 20 #2 100 0.339 1 5.027 2 0.942 6
      #23 #2 BiX-NAS 20 #1 100 0.380 5 6.397 7 0.947 7
      #24 #2 BiX-NAS 20 #2 100 0.339 1 5.027 3 0.936 8
      下载: 导出CSV

      表  5  不同模型的性能评估统计

      Table  5.   Performance evaluation statistics of different models

      NO. Dataset Model Epochs Params
      (×106)
      MACs
      (×109)
      Val_MIoU Training time(s)
      - #1 U-Net 100 7.762 8 13.719 4 0.969 1 1 039.206 9
      #11 #1 Phase1-Searched-Net(iter=1) 100 0.422 1 6.735 0 0.954 7 935.148 5
      #18 #1 BiX-NAS 100 0.339 1 5.027 2 0.955 2 829.392 7
      - #2 U-Net 100 7.762 8 13.719 4 0.958 6 2 000.413 2
      #12 #2 Phase1-Searched-Net(iter=1) 100 0.422 1 6.735 0 0.946 2 1 776.755 9
      #23 #2 BiX-NAS 100 0.380 5 6.397 7 0.947 7 1 710.181 1
      下载: 导出CSV
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    • 收稿日期:  2023-01-11
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