Shear wave structure of the Southern Volcanic Plateau of Oregon and Idaho and the Northern Great Basin of Nevada from surface wave dispersion

1982 ◽  
Vol 87 (B4) ◽  
pp. 2671 ◽  
Author(s):  
Keith F. Priestley ◽  
James N. Brune
Keyword(s):  
Surface Wave ◽  
Shear Wave ◽  
Great Basin ◽  
Wave Structure ◽  
Wave Dispersion ◽  
Surface Wave Dispersion

DisperNet: An Effective Method of Extracting and Classifying the Dispersion Curves in the Frequency–Bessel Dispersion Spectrum

2021 ◽  
Author(s):  
Sheng Dong ◽  
Zhengbo Li ◽  
Xiaofei Chen ◽  
Lei Fu
Keyword(s):  
Surface Wave ◽  
Dispersion Curve ◽  
Shear Wave ◽  
Wave Structure ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Surface Wave Dispersion ◽  
Transform Method ◽  
Practical Applications ◽  
Noise Data

ABSTRACT The subsurface shear-wave structure primarily determines the characteristics of the surface-wave dispersion curve theoretically and observationally. Therefore, surface-wave dispersion curve inversion is extensively applied in imaging subsurface shear-wave velocity structures. The frequency–Bessel transform method can effectively extract dispersion spectra of high quality from both ambient seismic noise data and earthquake events data. However, manual picking and semiautomatic methods for dispersion curves lack a unified criterion, which impacts the results of inversion and imaging. In addition, conventional methods are insufficiently efficient; more precisely, a large amount of time is required for curve extraction from vast dispersion spectra, especially in practical applications. Thus, we propose DisperNet, a neural network system, to extract and discriminate the different modes of the dispersion curve. DisperNet consists of two parts: a supervised network for dispersion curve extraction and an unsupervised method for dispersion curve classification. Dispersion spectra from ambient noise and earthquake events are applied in training and validation. A field data test and transfer learning test show that DisperNet can stably and efficiently extract dispersion curves. The results indicate that DisperNet can significantly improve multimode surface-wave imaging.

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>

Sign in / Sign up

Export Citation Format

Share Document