基于伴随方法的线性台阵背景噪声面波和远震体波联合成像研究

张超, 姚华建, 童平, 刘沁雅, 雷霆. 2020. 基于伴随方法的线性台阵背景噪声面波和远震体波联合成像研究. 地球物理学报, 63(11): 4065-4079, doi: 10.6038/cjg2020O0181
引用本文: 张超, 姚华建, 童平, 刘沁雅, 雷霆. 2020. 基于伴随方法的线性台阵背景噪声面波和远震体波联合成像研究. 地球物理学报, 63(11): 4065-4079, doi: 10.6038/cjg2020O0181
ZHANG Chao, YAO HuaJian, TONG Ping, LIU QinYa, LEI Ting. 2020. Joint inversion of linear array ambient noise surface-wave and teleseismic body-wave data based on an adjoint-state method. Chinese Journal of Geophysics (in Chinese), 63(11): 4065-4079, doi: 10.6038/cjg2020O0181
Citation: ZHANG Chao, YAO HuaJian, TONG Ping, LIU QinYa, LEI Ting. 2020. Joint inversion of linear array ambient noise surface-wave and teleseismic body-wave data based on an adjoint-state method. Chinese Journal of Geophysics (in Chinese), 63(11): 4065-4079, doi: 10.6038/cjg2020O0181

基于伴随方法的线性台阵背景噪声面波和远震体波联合成像研究

  • 基金项目:

    江苏省自然科学基金项目(BK20190499),河海大学中央高校基本科研业务费项目(2019B00714),国家自然科学基金项目(42004037),中国自然科学基金委重大项目课题(41790464)联合资助

详细信息
    作者简介:

    张超, 男, 1988年生, 河海大学讲师, 主要从事地震层析成像、结构反演等研究.E-mail:czhang18@hhu.edu.cn

    通讯作者: 姚华建, 男, 1979年生, 教授, 博士生导师, 主要从事地震波和背景噪声成像、岩石圈结构与变形、大地震破裂过程等领域的研究工作.E-mail:hjyao@ustc.edu.cn
  • 中图分类号: P315

Joint inversion of linear array ambient noise surface-wave and teleseismic body-wave data based on an adjoint-state method

More Information
  • 伴随层析成像(Adjoint Tomography)通过求解全波方程来准确模拟地震波在复杂介质中的传播,并利用波形信息来反演地下结构,是新一代的高分辨率成像方法.其中3-D伴随层析成像需要庞大的计算资源,而2-D反演相对更具计算效率.面波和远震体波是研究地壳上地幔速度结构的重要方法,它们对S波速度及Moho面的敏感度不同,通过联合反演,可以得到更为准确的S波速度结构及Moho面.通过两种数据的高度互补性,本文提出基于伴随方法的线性台阵背景噪声面波和远震体波联合成像方法,同时约束台阵下方S波速度结构及Moho面形态.我们将该方法应用到符合华北克拉通岩石圈典型结构特征的理论模型上,测试结果表明联合反演方法优势明显,相比于面波伴随层析成像,能获得更高分辨率的S波速度结构,同时能精准约束Moho面形态.相比于体波伴随层析成像,联合反演能有效压制高频假象,降低波形反演过程中的非线性化程度.本研究有望提供一种更为高效精准的线性台阵成像方法,搭建联合伴随层析成像理论框架,提升岩石圈成像分辨率,并为后续其他类型波形数据的引入提供思路和方法.

  • 加载中
  • 图 1 

    基于伴随方法的背景噪声面波经验格林函数和远震体波联合成像流程图

    Figure 1. 

    The workflow for joint inversion of ambient noise surface-wave empirical Green′s functions and teleseismic body-wave data based on the adjoint-state method

    图 2 

    三种类型伴随层析成像第1次迭代的S波速度敏感核函数及反演模型对比. (a)真实模型:地壳低速体模型,黑色虚线表示Moho面,上下层分别为地壳和地幔,模型参数参见表 1;(b)-(d)分别表示三种类型反演第1次迭代对应的S波速度敏感核函数;(e)-(g)分别表示三种类型反演第1次迭代产生的S波速度模型

    Figure 2. 

    The S-wave velocity sensitivity kernel and inverted model of the 1st iteration for 3 types of adjoint inversion. (a) True model: crust-over-mantle model with low-velocity anomaly in the crust; the black dashed line gives the Moho interface; the model parameters are given in Table 1; (b)-(d) The S wave velocity sensitivity kernel of the 1st iteration for 3 types of inversion; (e)-(g) The inverted S wave velocity model of the 1st iteration for 3 types of inversion.

    图 3 

    三种类型伴随层析成像最终反演模型对比. (a)-(c)不含噪声合成数据联合反演第1,4,8次迭代更新获得的S波速度模型;(d)不含噪声的体波反演的最终速度模型;(e)不含噪声的面波反演的最终S波速度模型; (f)含噪声合成数据联合反演成像结果:其中面波、体波噪声水平分别为2%和5%; (g) (a)-(f)速度模型中X=200 km处的S波速度垂直剖面

    Figure 3. 

    The final S wave velocity models for 3 types of inversion. (a)-(c) The inverted model after 1、4、5 and 8 iterations for joint inversion using synthetic data without noise; (d) The final inverted model for body wave tomography without noise; (e) The final inverted model for surface wave tomography without noise; (f) The inverted model for joint inversion with 2% random noise in surface waves and 5% random noise in body waves; (g) The vertical profile of S wave velocity located at X=200 km in the inverted model in (a)-(f)

    图 4 

    联合反演(不含噪声数据)第1,4,8次迭代波形拟合及数据误差下降曲线. (a)(c)(e)面波波形拟合; (b)(d)(f)体波波形拟合, 蓝、红线分别表示观测和理论波形;(g)数据误差下降曲线, 黑色、蓝色和红色五角星分别代表体波、面波和联合反演

    Figure 4. 

    Waveform fitting and reduction of data misfit for joint inversion without noise in the data. (a)(c)(e) Waveform fitting for surface waves; (b)(d)(f) Waveform fitting for teleseismic body waves; The blue and red waveforms are the observed and synthetic waveforms, respectively; (g) Reduction of data misfit values as a function of iteration number; The black, blue, and red stars represent results for body wave, surface wave, and joint inversion, respectively

    图 5 

    含起伏Moho界面的地壳-地幔模型联合反演. (a)真实模型,黑色实线表示Moho面;(b)-(d)分别为第1,4,7次迭代获得的S波速度模型; (e)-(g)分别为第9,12,15次迭代获得的S波速度模型;白色虚线表示更新的Moho界面

    Figure 5. 

    Joint inversion for the crust-mantle model with an undulating Moho interface. (a) True model, with the black solid line showing the location of Moho interface; (b)-(d) The inverted S wave velocity model after 1、4 and 7 iterations; (e)-(g) The inverted S wave velocity model after 9、12 and 15 iterations; The white dashed line denotes the updating Moho interface

    图 6 

    联合反演第一阶段和第二阶段反演波形拟合.蓝色实线表示观测数据,红色实线表示理论正演数据

    Figure 6. 

    Waveform fitting of the 1st and 2nd step for joint inversion. The blue solid line denotes the observed data, while the red solid line denotes the synthetic data

    图 7 

    联合反演数据误差下降曲线.蓝色和红色五角星分别代表第一阶段和第二阶段反演

    Figure 7. 

    Reduction of data misfit values as a function of iteration number for the 1st step (blue star) and the 2nd step (red star)

    图 8 

    华北克拉通地壳-地幔模型联合反演. (a)真实模型,中下地壳中存在一个低速体,相对速度扰动6%,低速体下方在X=200 km和X=350 km之间出现Moho面突降;为了分析联合反演方法对初始模型的依赖性程度,测试了两种不同的初始模型; (b)含起伏Moho面的地壳-地幔双层模型(简称model-1);(e)含水平Moho面的地壳-地幔双层模型(简称model-2); (c) (f)分别为基于model-1和model-2的联合反演结果;(d) (g)分别为基于model-1和model-2的面波单独反演结果

    Figure 8. 

    Joint inversion for the typical crust-mantle model of North China Craton. (a) True model, where a low velocity anomaly locates in the mid-lower crust with 6% velocity perturbation and there is a Moho depression between X=200 km and X=350km; In order to test the impact of initial model selection on the final model, we set up two different initial models: (b) the crust-mantle model with undulating Moho (model-1); (e) the crust-mantle model with flatting Moho (model-2); (c) (f) The final model for joint inversion based on model-1 and model-2; (d) (g) The final model for surface wave tomography based on model-1 and model-2

    图 9 

    联合反演第一阶段和第二阶段反演波形拟合.蓝色实线表示观测数据,红色实线表示理论正演数据

    Figure 9. 

    Waveform fitting of the 1st and 2nd step for joint inversion. The blue solid line denotes the observed data, while the red solid line denotes the synthetic data

    图 10 

    数据误差下降曲线.蓝色和红色五角星分别代表第一阶段和第二阶段反演

    Figure 10. 

    Reduction of data misfit values as a function of iteration number for the 1st step (blue star) and the 2nd step (red star)

    表 1 

    地壳-地幔双层模型参数

    Table 1. 

    Parameters in the two layer crust-mantle model

    密度
    /(kg·m-3)
    纵波
    /(m·s-1)
    横波
    /(m·s-1)
    厚度/km
    地壳(crust) 2600 5800 3198 60
    地幔(mantle) 3380 8080 4485 40
    下载: 导出CSV

    表 2 

    体波和面波反演中震源参数设置

    Table 2. 

    Source parameter settings for body wave and surface wave tomography

    体波反演 面波反演
    远震事件 入射角 子波类型 虚拟震源 震源位置(x, z) 子波类型
    Event 1
    Event 2
    Event 3
    Event 4
    Event 5
    Event 6
    Event 7
    Event 8
    16°
    20°
    24°
    28°
    332°
    336°
    340°
    344°

    Ricker
    子波
    (主频:
    0.4 Hz)
    Source 1
    Source 2
    Source 3
    Source 4
    Source 5
    Source 6
    Source 7
    Source 8
    (100 km, 0 km)
    (125 km, 0 km)
    (150 km, 0 km)
    (175 km, 0 km)
    (225 km, 0 km)
    (250 km, 0 km)
    (275 km, 0 km)
    (300 km, 0 km)

    高斯
    子波
    (主频:
    0.2 Hz)
    下载: 导出CSV

    表 3 

    数值模拟参数及反演计算时间统计

    Table 3. 

    Parameters of numerical modeling and computational time for inversion

    反演类型 联合反演
    (model-1)
    面波反演
    (model-1)
    联合反演
    (model-2)
    面波反演
    (model-2)
    模型大小
    (NX×NZ)
    X方向:500 km; Z方向: 100 km;
    NX=200; NZ=40
    正演参数 步数:12000, 步长: 0.02 s
    震源个数 32 16 32 16
    CPU耗费 128 64 128 64
    计算时间/
    迭代次数
    4.92 h
    /18次
    3.65 h
    /14次
    5.45 h
    /18次
    3.80 h
    /14次
    反演模型
    RMS误差
    5.41% 12.63% 6.68% 15.64%
    下载: 导出CSV
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出版历程
收稿日期:  2020-05-14
修回日期:  2020-08-05
上线日期:  2020-11-05

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