Wave Equation Inversion of Skeletonized SurfaceWaves

Zhang, Zhendong; Liu, Yike; Schuster, Gerard T.

doi:10.1190/segam2015-5805253.1

Cited by 14 publications

(11 citation statements)

References 10 publications

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“…The WD method presented in this paper is the adjoint-state method presented by (Zhang et al, 2015), who used a difference approximation to the gradient rather than an adjoint operation. Hence, our WD method is more than an order-of-magnitude faster for complicated models.…”

Section: Introductionmentioning

confidence: 99%

Skeletonized wave equation of surface wave dispersion inversion

Schuster

2016

SEG Technical Program Expanded Abstracts 2016

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SUMMARYWe present the theory for wave equation inversion of dispersion curves, where the misfit function is the sum of the squared differences between the wavenumbers along the predicted and observed dispersion curves. Similar to wave-equation traveltime inversion, the complicated surface-wave arrivals in traces are skeletonized as simpler data, namely the picked dispersion curves in the (k x , ω) domain. Solutions to the elastic wave equation and an iterative optimization method are then used to invert these curves for 2D or 3D velocity models. This procedure, denoted as wave equation dispersion inversion (WD), does not require the assumption of a layered model and is less prone to the cycle skipping problems of full waveform inversion (FWI). The synthetic and field data examples demonstrate that WD can accurately reconstruct the S-wave velocity distribution in laterally heterogeneous media.

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Section: Introductionmentioning

confidence: 99%

Skeletonized wave equation of surface wave dispersion inversion

Schuster

2016

SEG Technical Program Expanded Abstracts 2016

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“…The philosophy is to find equivalent data that preserve the key features of the original one but have fewer oscillations in time. The optimal transport approach (Métivier et al, 2016;Yang et al, 2016), the Fourier domain method (Solano et al, 2013), and the skeletonized dispersion curve inversion (Zhang et al, 2015(Zhang et al, , 2016 fall into this category. (4) Joint inversion.…”

Section: Introductionmentioning

confidence: 99%

Normalized nonzero-lag crosscorrelation elastic full-waveform inversion

Zhang

Alkhalifah

et al. 2019

GEOPHYSICS

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He B, et al. (2018) Normalized nonzero-lag crosscorrelation elastic full-waveform inversion.ABSTRACT Full-waveform inversion (FWI) is an attractive technique due to its ability to build high-resolution velocity models. Conventional amplitude-matching FWI approaches remain challenging because the simplified computational physics used does not fully represent all wave phenomena in the earth. Because the earth is attenuating, a sample-by-sample fitting of the amplitude may not be feasible in practice. We have developed a normalized nonzero-lag crosscorrelataion-based elastic FWI algorithm to maximize the similarity of the calculated and observed data. We use the first-order elastic-wave equation to simulate the propagation of seismic waves in the earth. Our proposed objective function emphasizes the matching of the phases of the events in the calculated and observed data, and thus, it is more immune to inaccuracies in the initial model and the difference between the true and modeled physics. The normalization term can compensate the energy loss in the far offsets because of geometric spreading and avoid a bias in estimation toward extreme values in the observed data. We develop a polynomial-type weighting function and evaluate an approach to determine the optimal time lag. We use a synthetic elastic Marmousi model and the BigSky field data set to verify the effectiveness of the proposed method. To suppress the short-wavelength artifacts in the estimated S-wave velocity and noise in the field data, we apply a Laplacian regularization and a total variation constraint on the synthetic and field data examples, respectively.

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“…1). In addition, the surface-wave inversion method presented in this paper is the adjointstate implementation of WD, which is different from Zhang et al (2015Zhang et al ( , 2016, who used a difference approximation to calculate the misfit gradient model. Hence, our WD method is more general and more than an order-of-magnitude faster for complicated models.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, our WD method is more general and more than an order-of-magnitude faster for complicated models. Here the phase velocity is C(ω) = ω/κ(ω) and κ(ω) is the skeletonized data (Zhang et al 2015;Schuster 2017). In the following sections, we first present the theory of WD and a workflow for its practical implementation. The WD method is then validated with tests on both synthetic data and field data.…”

Section: Introductionmentioning

confidence: 99%