| 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
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Joint inversion of linear array ambient noise surface-wave and teleseismic body-wave data based on an adjoint-state method
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Abstract
Adjoint tomography is one of state-of-the-art imaging methods with high resolution. It can get better-resolved models by solving full wave equations to accurately simulate the propagation of seismic waves, and by considering full waveform information in inversion. However, the computational cost and storage requirement of 3-D adjoint tomography are high. Relatively, 2-D adjoint tomography is much more computationally efficient. Surface waves and teleseismic body waves provide essential data for studying crustal and uppermost mantle structures. Due to different sensitivities to shear wave velocity at depths and Moho discontinuity, joint inversion of both datasets can resolve the Vs model and Moho interface better. To take advantages of the two different types of data, we propose a strategy of joint inversion for ambient noise surface waves and teleseismic body waves recorded by linear arrays based on the adjoint-state method, which can be used to yield a fine Vs model and Moho topography. We perform various synthetic imaging experiments, in which the model has typical features of crustal structures in North China Craton (NCC). Compared to the surface wave inversion only, joint inversion improves the resolution of images as well as constraining discontinuity undulations better. Compared to the body wave inversion only, joint inversion can suppress high frequency artifacts and reduce the nonlinearity during inversion. This study could provide an efficient alternative to image fine velocity structures beneath linear arrays and also build a framework for joint inversion. It could improve the resolution of lithospheric imaging and also provide strategies of incorporating other waveforms in the future.
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Keywords:
- Adjoint tomogramphy /
- Ambient noise /
- Teleseismic body wave /
- Joint inversion /
- Seismic linear array
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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
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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
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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.
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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)
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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
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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
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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
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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)
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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
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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
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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)