Exploring nonlinearity and nonuniqueness in surface-wave inversion for near-surface velocity estimation

2013 ◽  
Vol 32 (6) ◽  
pp. 648-655 ◽  
Author(s):  
Huub Douma ◽  
Matthew M. Haney
Keyword(s):  
Surface Wave ◽  
Velocity Estimation ◽  
Surface Velocity ◽  
Near Surface ◽  
Wave Inversion

Near-surface shear-wave velocity estimation based on surface-wave inversion

2016 ◽  
Vol 35 (11) ◽  
pp. 940-945 ◽  
Author(s):  
Peiming Li ◽  
Kui Zhang ◽  
Yimeng Zhang ◽  
Zhihui Yan
Keyword(s):  
Surface Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Velocity Estimation ◽  
Surface Shear ◽  
Near Surface ◽  
Wave Inversion ◽  
Surface Shear Wave

Surface-Wave Simulation for the Continuously Moving Seismic Sources

2021 ◽  
Author(s):  
Yuefeng Yan ◽  
Chengyu Sun ◽  
Tengfei Lin ◽  
Jiao Wang ◽  
Jidong Yang ◽  
...  
Keyword(s):  
Rayleigh Waves ◽  
Frequency Dispersion ◽  
Dispersion Curves ◽  
Velocity Estimation ◽  
Surface Velocity ◽  
Seismic Sources ◽  
Near Surface ◽  
Moving Source ◽  
Source Data ◽  
Doppler Effects

Abstract In exploration and earthquake seismology, most sources used in subsurface structure imaging and rock property estimation are fixed in certain positions. Continuously moving seismic sources, such as vehicles and the metro, are one kind of important passive sources in ambient noise research. Commonly, seismic data acquisition and processing for moving sources are based on the assumption of simple point passive sources, and the dispersion curve inversion is applied to constrain near-surface velocity. This workflow neglects the Doppler effects. Considering the continuously moving properties of the sources, we first derive the analytical solution for the Rayleigh waves excited by heavy vehicles and then analyze their Doppler effects and dispersion curves. We observe that the moving source data have the Doppler effect when compared with the changes in the frequency of the source intensity, but this effect does not affect the frequency dispersion of Rayleigh waves. The dispersion curves computed for moving source records are consistent with the analytical dispersion solutions, which provide a theoretical foundation for velocity estimation using moving source data.

Slant f-k transform of multichannel seismic surface wave data

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. A19-A24 ◽  
Author(s):  
Aleksander S. Serdyukov ◽  
Aleksander V. Yablokov ◽  
Anton A. Duchkov ◽  
Anton A. Azarov ◽  
Valery D. Baranov
Keyword(s):  
Surface Wave ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Velocity Estimation ◽  
Seismic Exploration ◽  
Spectral Processing ◽  
Near Surface ◽  
Frequency Time Representation ◽  
Time Representation ◽  
S Transform

We have addressed the problem of estimating surface-wave phase velocities through the spectral processing of seismic data. This is the key step of the well-known near-surface seismic exploration method, called multichannel analysis of surface waves. To increase the accuracy and ensure the unambiguity of the selection of dispersion curves, we have developed a new version of the frequency-wavenumber ([Formula: see text]-[Formula: see text]) transform based on the S-transform. We obtain the frequency-time representation of seismic data. We analyze the obtained S-transform frequency-time representation in a slant-stacking manner but use a spatial Fourier transform instead of amplitude stacking. Finally, we build the [Formula: see text]-[Formula: see text] image by analyzing the spatial spectra for different steering values of the surface-wave group velocities. The time localization of the surface-wave packet at each frequency increases the signal-to-noise ratio because of an exclusion of noise in other time steps (which does not fall in the effective width of the corresponding wavelet). The new [Formula: see text]-[Formula: see text] transform, i.e., the slant [Formula: see text]-[Formula: see text] (SFK) transform, renders a better spectral analysis than the conventional [Formula: see text]-[Formula: see text] transform and yields more accurate phase-velocity estimation, which is critical for the surface-wave analysis. The advantages of the SFK transform have been confirmed by synthetic- and field-data processing.

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