Shear-wave velocity measurement of soils using Rayleigh waves

1992 ◽  
Vol 29 (4) ◽  
pp. 558-568 ◽  
Author(s):  
K. O. Addo ◽  
P. K. Robertson
Keyword(s):  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Rayleigh Waves ◽  
Ground Surface ◽  
Wave Dispersion ◽  
Velocity Variation ◽  
Surface Wave Dispersion ◽  
Waveform Data ◽  
Cone Penetration Tests

A modified version of the spectral analysis of surface waves (SASW) equipment and analysis procedure has been developed to determine in situ shear-wave velocity variation with depth from the ground surface. A microcomputer has been programmed to acquire waveform data and perform the relevant spectral analyses that were previously done by signal analyzers. Experimental dispersion for Rayleigh waves is now obtainable at a site and inverted with a fast algorithm for dispersion computation. Matching experimental and theoretical dispersion curves has been automated in an optimization routine that does not require intermittent operator intervention or experience in dispersion computation. Shear-wave velocity profiles measured by this procedure are compared with results from independent seismic cone penetration tests for selected sites in western Canada. Key words : surface wave, dispersion, inversion, optimization, shear-wave velocity.

Surface-Wave Dispersion Approach for Evaluating Statistical Models That Account for Shear-Wave Velocity Uncertainty

2016 ◽  
Vol 142 (11) ◽  
pp. 04016061 ◽  
Author(s):  
Shawn C. Griffiths ◽  
Brady R. Cox ◽  
Ellen M. Rathje ◽  
David P. Teague
Keyword(s):  
Surface Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Statistical Models ◽  
Wave Dispersion ◽  
Surface Wave Dispersion

scholarly journals Probabilistic neural network tomography across Grane field (North Sea) from surface wave dispersion data

2020 ◽  
Vol 223 (3) ◽  
pp. 1741-1757
Author(s):  
S Earp ◽  
A Curtis ◽  
X Zhang ◽  
F Hansteen
Keyword(s):  
Neural Network ◽  
Monte Carlo ◽  
Surface Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
North Sea ◽  
Shear Wave Velocity ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Dispersion Data

SUMMARY Surface wave tomography uses measured dispersion properties of surface waves to infer the spatial distribution of subsurface properties such as shear wave velocities. These properties can be estimated vertically below any geographical location at which surface wave dispersion data are available. As the inversion is significantly non-linear, Monte Carlo methods are often used to invert dispersion curves for shear wave velocity profiles with depth to give a probabilistic solution. Such methods provide uncertainty information but are computationally expensive. Neural network (NN) based inversion provides a more efficient way to obtain probabilistic solutions when those solutions are required beneath many geographical locations. Unlike Monte Carlo methods, once a network has been trained it can be applied rapidly to perform any number of inversions. We train a class of NNs called mixture density networks (MDNs), to invert dispersion curves for shear wave velocity models and their non-linearized uncertainty. MDNs are able to produce fully probabilistic solutions in the form of weighted sums of multivariate analytic kernels such as Gaussians, and we show that including data uncertainties as additional inputs to the MDN gives substantially more reliable velocity estimates when data contains significant noise. The networks were applied to data from the Grane field in the Norwegian North sea to produce shear wave velocity maps at several depth levels. Post-training we obtained probabilistic velocity profiles with depth beneath 26 772 locations to produce a 3-D velocity model in 21 s on a standard desktop computer. This method is therefore ideally suited for rapid, repeated 3-D subsurface imaging and monitoring.

Layered anisotropy beneath the Japan Sea and NE China from inversion of surface wave dispersion using rj-MCMC method

2020 ◽  
Author(s):  
Yanzhe Zhao ◽  
Zhen Guo ◽  
Xingli Fan ◽  
Yanbin Wang
Keyword(s):  
Surface Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Japan Sea ◽  
Wave Dispersion ◽  
Azimuthal Anisotropy ◽  
Ne China ◽  
Surface Wave Dispersion ◽  
The Japan Sea

<p>The surface wave dispersion data with azimuthal anisotropy can be used to invert for the wavespeed azimuthal anisotropy, which provides essential dynamic information about depth-varying deformation of the Earth’s interior. In this study, we adopt an rj-MCMC (reversible jump Markov Chain Monte Carlo) technique to invert for crustal and upper mantle shear velocity and azimuthal anisotropy beneath the Japan Sea using Rayleigh wave azimuthally anisotropic phase velocity measurements from Fan et al. (2019). The rj-MCMC implements trans-dimensional sampling in the whole model space and derives the distribution for each model parameter (shear wave velocity and anisotropy parameters) directly from data. Without the prejudiced parameterization for model, the result can be more credible, from which some more reliable estimates for shear wave velocity and azimuthal anisotropy as well as their uncertainties can be acquired. Our preliminary results, together with shear wave splitting observations, show a layered anisotropy beneath the Japan Sea and NE China, suggesting the complicated mantle flow that is controlled by the subduction of the Pacific plate and the large-scale upwelling beneath the Changbaishan volcano.</p>

Mapping formation radial shear-wave velocity variation by a constrained inversion of borehole flexural-wave dispersion data

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. E183-E190 ◽  
Author(s):  
Xiao-Ming Tang ◽  
Douglas J. Patterson
Keyword(s):  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
High Frequency ◽  
Wave Dispersion ◽  
Velocity Variation ◽  
Flexural Wave ◽  
Inversion Method ◽  
Dispersion Characteristics ◽  
Dispersion Data

We have developed a novel constrained inversion method for estimating a radial shear-wave velocity profile away from the wellbore using dipole acoustic logging data and have analyzed the effect of the radial velocity changes on dipole-flexural-wave dispersion characteristics. The inversion of the dispersion data to estimate the radial changes is inherently a nonunique problem because changing the degree of variation or the radial size of the variation zone can produce similar wave-dispersion characteristics. Nonuniqueness can be solved by developing a constrained inversion method. This is done by constraining the high-frequency portion of the model dispersion curve with another curve calculated using the near-borehole velocity. The constraint condition is based on the physical principle that a high-frequency dipole wave has a shallow penetration depth and is therefore sensitive to the near-borehole shear-wave velocity. We have validated the result of the constrained inversion with synthetic data testing. Combining the new inversion method with four-component crossed-dipole anisotropy processing obtains shear radial profiles in fast and slow shear polarization directions. In a sandstone formation, the fast and slow shear-wave profiles show substantial differences caused by the near-borehole stress field, demonstrating the ability of the technique to obtain radial and azimuthal geomechanical property changes near the wellbore.

scholarly journals Study on linearized inversion of surface wave dispersion for estimating near-surface shear wave velocity

2019 ◽  
Vol 311 ◽  
pp. 012055
Author(s):  
Aditya Lesmana ◽  
Awali Priyono ◽  
Tedi Yudistira ◽  
Anisa Agustina
Keyword(s):  
Surface Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Surface Shear ◽  
Near Surface ◽  
Surface Shear Wave

Using Deep Learning to Derive Shear-Wave Velocity Models from Surface-Wave Dispersion Data

2020 ◽  
Vol 91 (3) ◽  
pp. 1738-1751
Author(s):  
Jing Hu ◽  
Hongrui Qiu ◽  
Haijiang Zhang ◽  
Yehuda Ben-Zion
Keyword(s):  
Surface Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Southern California ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Velocity Models ◽  
Dispersion Data ◽  
Phase Group

Abstract We present a new algorithm for derivations of 1D shear-wave velocity models from surface-wave dispersion data using convolutional neural networks (CNNs). The technique is applied for continental China and the plate boundary region in southern California. Different CNNs are designed for these two regions and are trained using theoretical Rayleigh-wave phase and group velocity images computed from reference 1D VS models. The methodology is tested with 3260 phase–group images for continental China and 4160 phase–group images for southern California. The conversions of these images to velocity profiles take ∼23  s for continental China and ∼30  s for southern California on a personal laptop with the NVIDIA GeForce GTX 1060 core and a memory of 6 GB. The results obtained by the CNNs show high correlation with previous studies using conventional methods. The effectiveness of the CNN technique makes this fast method an important alternative for deriving shear-wave velocity models from large datasets of surface-wave dispersion data.

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