Wave equation dispersion inversion using a difference approximation to the dispersion-curve misfit gradient

Zhang, Zhendong; Schuster, Gerard T.; Liu, Yike; Hanafy, Sherif M.; Li, Jing

doi:10.1016/j.jappgeo.2016.07.019

Cited by 25 publications

(16 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Normalized nonzero-lag crosscorrelation elastic full-waveform inversion

Zhang

Alkhalifah

et al. 2019

GEOPHYSICS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Acausal and causal stacking can remove coherent acausal signals, which has been discussed by Bensen et al (2007). After obtaining high-quality common virtual-shot gathers, we use waveequation dispersion spectrum inversion to estimate the S-wave velocities in the near-surface (Zhang et al, 2016;Zhang and Alkhalifah, 2018).…”

Section: Introductionmentioning

confidence: 99%

Near-surface S-wave velocity estimation using ambient noise from fiber-optic acquisition

Zhang

Alajami

Alkhalifah

2019

SEG Technical Program Expanded Abstracts 2019

Self Cite

View full text Add to dashboard Cite

Distributed acoustic sensing (DAS) acquisition measures the ground motion using fiber-optic cables. Unlike conventional sensors, DAS can cost effectively provide dense seismic arrays and long-term operations, which is good for monitoring ambient noise. We propose a similarity-weighted stacking of randomly selected short-time duration noise to generate virtual common-shot-gathers (CSG). The similarity-weighted stacking only counts the primary contributions of coherent events, while a short-time correlation can suppress the crosstalk usually presents in late arrivals. Then, we use the wave-equation Rayleigh-wave dispersion-spectrum inversion, which utilizes all the dispersion modes available and avoids picking the dispersion curve, estimating the shallow S-wave velocities. We use a field DAS data set collected in Saudi Arabia to demonstrate the proposed method.

show abstract

“…It, however, has inherent weaknesses in its application to field data. The L 2 norm objective function requires that the initial model should be kinematically reasonably accurate; otherwise, the inverse problem yields models corresponding to local minima when using a gradient-based optimization (Zhang et al, 2015(Zhang et al, , 2016. The multiscale inversion strategy implemented in different scenarios helps to avoid converging to a local minimum and it is usually implemented by gradually increasing the inversion frequency (Bunks et al, 1995).…”

Section: Introductionmentioning

confidence: 99%