Imaging near-surface heterogeneities by natural migration of surface waves

Liu, Zhaolun; AlTheyab, Abdullah; Hanafy, Sherif M.; Schuster, Gerard T.

doi:10.1190/segam2016-13845123.1

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…Figure 8a next to the conjectured fault is consistent with the downthrown-side of an interpreted normal fault. The LVZ is also consistent with the reflectivities of the migration image (Liu et al, 2016(Liu et al, , 2017 indicated by the blue zone next to the dashed fault in Figure 8b. This LVZ next to the fault is accompanied by a delay in the surface-wave arrivals shown in the common offset gathers (COG) in Figure 8c.…”

Section: Qademah Fault Seismic Datasupporting

confidence: 81%

3D wave-equation dispersion inversion of surface waves

Liu

Hanafy

et al. 2018

SEG Technical Program Expanded Abstracts 2018

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Section: Qademah Fault Seismic Datasupporting

confidence: 81%

3D wave-equation dispersion inversion of surface waves

Liu

Hanafy

et al. 2018

SEG Technical Program Expanded Abstracts 2018

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“…The blue low-velocity zone (LVZ) in Figure 8a next to the conjectured fault is consistent with the downthrown-side of an interpreted normal fault. The LVZ is also consistent with the reflectivities of the migration image (Liu et al, 2016(Liu et al, , 2017 indicated by the blue zone next to the dashed fault in Figure 8b. This LVZ next to the fault is accompanied by a delay in the surface-wave arrivals shown in the common offset gathers (COG) in Figure 8c.…”

Section: Numerical Examples Modified Foothills Modelsupporting

confidence: 81%

3D wave-equation dispersion inversion of surface waves

Liu

Schuster

2017

SEG 2017 Workshop: Full-Waveform Inversion and Beyond, Beijing, China, 20-22 November 2017

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The 2D wave-equation dispersion inversion (WD) methodology is extended to the inversion of three-dimensional data for a 3D shear-wave velocity model. The objective function of 3D WD is the sum of the squared wavenumber differences along each azimuth angle between the predicted and observed 3D dispersion curves. The 3D dispersion curves are obtained by wavenumber-frequency analysis of the fundamental Rayleigh waves in each 3D shot gather. The S-wave velocity update is computed by a weighted zero-lag crosscorrelation between the source wavefield and the back-projected receiver-side wavefield for each azimuth angle. The synthetic and field data examples demonstrate that the 3D WD method can accurately estimate the 3D S-wave velocity model in laterally heterogeneous media.

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“…In recent years, shallow seismic exploration has attracted increasing attention (Liu, et al ., 2016; Feigenbaum, et al ., 2017; Oktariena and Triyoso, 2018; Wo, et al ., 2018; Zheng, 2018; Gallais, et al ., 2019; Mahvelati, et al ., 2019; Shakuro, 2019; Song, et al ., 2019; Wang, 2019; Zhenbo, et al ., 2019; Sun, et al ., 2020). Near‐surface structures are complex, and the seismic wave fields are rich and variable, which cause difficulties for seismic migration (Almuhaidib and Toksöz, 2014; Krohn and Murray, 2016; Dehghannejad, et al ., 2017; Alam, 2019).…”

Section: Introductionmentioning

confidence: 99%

A numerical dispersion‐suppressed method for shallow seismic migration

Wang

et al. 2020

Near Surface Geophysics

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Reverse time migration can accurately image underground earth structures. However, for shallow seismic exploration, the seismic wave velocity is often lower than the velocity in the middle‐deep layers, which causes numerical dispersion for finite‐difference schemes and leads to poor seismic imaging quality. Suppressing numerical dispersion by grid encryption or increasing the finite‐difference order seriously reduces computational efficiency, which is not the optimal solution. To improve the imaging quality without sacrificing computational efficiency, a regularization factor is added to the acoustic wave equation to correct the phase velocity of high wave‐number components. An appropriate regularization factor can eliminate numerical dispersion that results from large grid interval schemes and can reduce the size of computational grids and improve computational efficiency. For simulations in the frequency domain, reducing the grid size also means reducing computer memory requirements. Numerical experiments indicate that the regularization factor should match the degree of numerical dispersion. Larger regularization factors can suppress serious numerical dispersion. However, excessively large regularization factors may destroy the effective wave field. Different numerical models verify the effectiveness of the improved acoustic equation for suppressing numerical dispersion and maintaining amplitudes and provide a novel means to improve the shallow seismic image quality.

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Imaging near-surface heterogeneities by natural migration of surface waves

Cited by 5 publications

References 10 publications

3D wave-equation dispersion inversion of surface waves

3D wave-equation dispersion inversion of surface waves

3D wave-equation dispersion inversion of surface waves

A numerical dispersion‐suppressed method for shallow seismic migration

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