Sensitivity analysis of dispersion curves of Rayleigh waves with fundamental and higher modes

Pan, L.; Chen, Xiaofei; Wang, Jiannan; Yang, Zhentao; Zhang, Dazhou

doi:10.1093/gji/ggy479

Cited by 76 publications

(44 citation statements)

References 24 publications

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“…Second, for each given initial model, we employ a quasi‐Newton method named the Broyden ‐Fletcher‐Goldfarb‐Shanno (BFGS) algorithm (e.g., Byrd et al, ) to perform the nonlinear inversion. The gradient of the misfit function and the sensitivity function of Vs are detailed in Pan et al ().…”

Section: Inversion Methodsmentioning

confidence: 99%

“…The inversion model is assumed to be a horizontally flat multilayered half‐space elastic medium. In principle, the densities and P wave velocities in each layer could affect the phase velocities, but their sensitivities to the phase velocity are much smaller than that of the S wave velocity (Vs) (e.g., Pan et al, ; Xia et al, ; Xia et al, ). Therefore, we will invert for Vs while treating the P wave velocities and densities as known by using empirical dependencies (e.g., Brocher, ).…”

Section: Inversion Methodsmentioning

confidence: 99%

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Shear Velocity Inversion Using Multimodal Dispersion Curves From Ambient Seismic Noise Data of USArray Transportable Array

Pan

Wang

et al. 2020

JGR Solid Earth

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We utilize an array method called the frequency‐Bessel transformation method to extract the multimodal dispersion curves of Rayleigh waves from ambient seismic noise data recorded by the USArray Transportable Array. We observe as many as five overtones' dispersion curves of Rayleigh waves in the Midwestern United States, and four and three overtones' dispersion curves, respectively, in the U.S. Great Plain area and Northeastern United States. We employ a quasi‐Newton method to invert the multimodal dispersion curves for the shear velocity. We find that the sensitivity of overtones to deeper structures is higher than that of fundamental mode in the same frequency range. The utilization of overtones significantly improved the non‐uniqueness and convergence of the inversions, which make the final inversion results robust and reliable. Our inversion results for S wave velocity (Vs) model in the studied areas exhibit some differences compared with Shen and Ritzwoller's model (2016, https://doi.org/10.1002/2016JB012887). The Vs falls abruptly in the lower crust (21–33 km) in the U.S. Great Plain area and Northeastern U.S. areas. A high‐velocity zone is observable in depth of 50–60 km in the U.S. Great Plain area and Midwestern United States, and all three models show larger Vs values below 50 km than those of Shen and Ritzwoller (2016).

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Section: Inversion Methodsmentioning

confidence: 99%

Section: Inversion Methodsmentioning

confidence: 99%

Shear Velocity Inversion Using Multimodal Dispersion Curves From Ambient Seismic Noise Data of USArray Transportable Array

Pan

Wang

et al. 2020

JGR Solid Earth

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“…In the ANT, it is a key step to extract the surface wave dispersion curves from the noise cross‐correlation functions (CCFs). Besides of the currently widely used fundamental Rayleigh wave dispersion obtained from the vertical‐vertical (ZZ) CCFs, other quantities, such as higher‐mode Rayleigh/Love wave dispersion data and Rayleigh wave ellipticity from multicomponent CCFs, provide complementary constraint on velocity structures (Okada, 2003; Pan et al., 2018; Savage et al., 2013; Wang et al., 2019; Xia, 2014; Yao et al., 2011) and can help to resolve radial anisotropy (Huang et al., 2010; Hu et al., 2020; Lin et al., 2008; Moschetti et al., 2010).…”

Section: Introductionmentioning

confidence: 99%

The Frequency‐Bessel Spectrograms of Multicomponent Cross‐Correlation Functions From Seismic Ambient Noise

Luo

Yao

2020

JGR Solid Earth

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The recently developed frequency‐Bessel transformation (F‐J) method is effective to extract multimode surface wave dispersion curves from ambient noise cross‐correlation functions (CCFs). However, this method is currently limited to the vertical‐vertical component CCFs, and only Rayleigh wave dispersion curves can be obtained. In this study, we first relate the F‐J spectrogram to the spatial autocorrelation coefficients; we then extend the F‐J method to the full multicomponent CCFs tensor, including the radial‐radial, transverse‐transverse, and the mixed‐component CCFs. Using the newly derived formulation, not only the signal of higher‐mode Rayleigh wave phase velocity dispersion can be enhanced, but also the multimode Love wave phase velocity dispersion curves and the higher‐mode Rayleigh wave ellipticity can be extracted. The formulation is tested in several numerical examples and is applied to field data in North America. Our derivation and formulation provide an extension to the current F‐J method and help to take usage of multicomponent CCFs. The resulting higher‐mode surface wave dispersions and Rayleigh wave ellipticity provide complementary constraint on the Earth structures.

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“…The particular case of a horizontal homogeneous medium, that is a finite‐layer stratified velocity model that can be described by four vectors of parameters, is considered:

{boldV}_{boldS} = false(V_{S}^{1}, \dots, V_{S}^{N} false), {boldV}_{boldP} = false(V_{S}^{1}, \dots, V_{S}^{N} false), bold-italicρ = false(ρ^{1}, \dots, ρ^{N} false), h = false(h^{1}, \dots, h^{N - 1} false)

, where the last N th layer is the half‐space. Since the Rayleigh wave phase velocity is much less sensitive to

V_{P}

and ρ than to

V_{S}

and h (Pan et al ., 2018), we follow a common approach and assume the constant velocity ratio

V_{P} / V_{S} = Const = \sqrt{2 (1 - ν) / (1 - 2 ν)}

, where ν is Poisson's ratio. In our research, which also includes synthetic and field data processing examples (presented below), we set

ν = 0.35

and

ρ = Const = 1900

kg/m 3 .…”

Section: Methodsmentioning

confidence: 99%

An artificial neural network approach for the inversion of surface wave dispersion curves

Yablokov

Serdyukov

Loginov

et al. 2021

Geophysical Prospecting

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We describe a new algorithm for the inversion of one‐dimensional shear‐wave velocity profiles from dispersion curves of the fundamental mode of Rayleigh surface waves. The novelties of our approach are that the layer velocities and thicknesses are set as unknowns, and an artificial neural network is proposed to solve the inverse problem. We suggest that training data should be calculated for a set of random synthetic velocity layered models, while layer thicknesses and velocities should be set to fixed intervals, with ranges estimated based on the systematic application of empirical relations between Rayleigh and S‐wave velocities to the dispersion data. Our main challenge is a total overhaul of the artificial neural network, which includes selecting the optimal artificial neural network architecture and parameters by performing a large number of numerical experiments. Our synthetic results show that the accuracy of the proposed approach outperforms that of the Monte Carlo approach. We illustrate our proposed method with West Siberia data processing obtained from an area of approximately 800 km2. From a user perspective, the main strength of our method is the computationally efficient processing of large amounts of dispersion data, which make it well suited for four‐dimensional near‐surface monitoring.

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Sensitivity analysis of dispersion curves of Rayleigh waves with fundamental and higher modes

Cited by 76 publications

References 24 publications

Shear Velocity Inversion Using Multimodal Dispersion Curves From Ambient Seismic Noise Data of USArray Transportable Array

Shear Velocity Inversion Using Multimodal Dispersion Curves From Ambient Seismic Noise Data of USArray Transportable Array

The Frequency‐Bessel Spectrograms of Multicomponent Cross‐Correlation Functions From Seismic Ambient Noise

An artificial neural network approach for the inversion of surface wave dispersion curves

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