scholarly journals Lithospheric shear wave velocity and radial anisotropy beneath the northern part of North China from surface wave dispersion analysis

2015 ◽  
Vol 16 (8) ◽  
pp. 2619-2636 ◽  
Author(s):  
Yuanyuan V. Fu ◽  
Yuan Gao ◽  
Aibing Li ◽  
Yutao Shi
Keyword(s):  
Surface Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
North China ◽  
Wave Dispersion ◽  
Dispersion Analysis ◽  
Surface Wave Dispersion ◽  
Radial Anisotropy

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>

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.

Exploring the model space and ranking a best class of models in surface-wave dispersion inversion: Application at European strong-motion sites

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. B147-B166 ◽  
Author(s):  
Giuseppe Di Giulio ◽  
Alexandros Savvaidis ◽  
Matthias Ohrnberger ◽  
Marc Wathelet ◽  
Cecile Cornou ◽  
...  
Keyword(s):  
Surface Wave ◽  
Velocity Profile ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Strong Motion ◽  
Model Space ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Shear Wave Velocity Profile

The inversion of surface-wave dispersion curve to derive shear-wave velocity profile is a very delicate process dealing with a nonunique problem, which is strongly dependent on the model space parameterization. When independent and reliable information is not available, the selection of most representative models within the ensemble produced by the inversion is often difficult. We implemented a strategy in the inversion of dispersion curves able to investigate the influence of the parameterization of the model space and to select a “best” class of models. We analyzed surface-wave dispersion curves measured at 14 European strong-motion sites within the NERIES EC-Project. We focused on the inversion task exploring the model space by means of four distinct parameterization classes composed of layers progressively added over a half-space. The classes differ in the definition of the shear-wave velocity profile; we considered models with uniform velocity as well as models with increasing velocity with depth. At each site and for each model parameterization, we performed an extensive surface-wave inversion (200,100 models for five seeds) using the conditional neighborhood algorithm. We addressed the model evaluation following the corrected Akaike’s information criterion (AICc) that combines the concept of misfit to the number of degrees of freedom of the system. The misfit was computed as least-squares estimation between theoretical and observed dispersion curve. The model complexity was accounted in a penalty term by AICc. By applying such inversion strategy on 14 strong-motion sites, we found that the best parameterization of the model space is mostly three to four layers over a half-space; where the shear-wave velocity of the uppermost layers can follow uniform or power-law dependence with depth. The shear-wave velocity profiles derived by inversion agree with shear-wave velocity profiles provided by borehole surveys at approximately 80% of the sites.

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