scholarly journals Shear wave velocity structure in West Java, Indonesia as inferred from surface wave dispersion

2016 ◽  
Author(s):  
Titi Anggono ◽  
Syuhada
Keyword(s):  
Surface Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Velocity Structure ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
West Java ◽  
Shear Wave Velocity Structure

scholarly journals Rapid assessment of S-wave profiles from the inversion of multichannel

1998 ◽  
Vol 41 (1) ◽  
Author(s):  
G. A. Tselentis ◽  
G. Delis
Keyword(s):  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Velocity Structure ◽  
Rapid Assessment ◽  
Detailed Knowledge ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Wave Profiles ◽  
Shear Wave Velocity Structure

The importance of detailed knowledge of the shear-wave velocity structure of the upper geological layers was recently stressed in strong motion studies. In this work we describe an algorithm which we have developed to infer the 1D shear wave velocity structure from the inversion of multichannel surface wave dispersion data (ground-roll). Phase velocities are derived from wavenumber-frequency stacks while the inversion process is speeded up by the use of Householder transformations. Using synthetic and experimental data, we examined the applicability of the technique in deducing S-wave profiles. The comparison of the obtained results with those derived from cross-hole measurements and synthesized wave fields proved the reliability of the technique for the rapid assessment of shear wave profiles during microzonation investigations.

scholarly journals Shear-wave velocity structure at Mt. Etna from inversion of Rayleigh-wave dispersion patterns (2 s < T < 20 s)

2010 ◽  
Vol 53 (2) ◽  
Author(s):  
Luigia Cristiano ◽  
Simona Petrosino ◽  
Gilberto Saccorotti ◽  
Matthias Ohrnberger ◽  
Roberto Scarpa
Keyword(s):  
Rayleigh Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Velocity Structure ◽  
Wave Dispersion ◽  
Dispersion Patterns ◽  
Mt Etna ◽  
Rayleigh Wave Dispersion ◽  
Shear Wave Velocity Structure

Broadband Rayleigh-Wave Dispersion Curve and Shear-Wave Velocity Structure for Yucca Flat, Nevada

2012 ◽  
Vol 102 (4) ◽  
pp. 1361-1372 ◽  
Author(s):  
K. A. Schramm ◽  
R. E. Abbott ◽  
M. Asten ◽  
S. Bilek ◽  
A. Pancha ◽  
...  
Keyword(s):  
Rayleigh Wave ◽  
Dispersion Curve ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Velocity Structure ◽  
Wave Dispersion ◽  
Rayleigh Wave Dispersion ◽  
Shear Wave Velocity Structure

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.

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