Seismic velocity site characterization of 10 Arizona strong-motion recording stations by spectral analysis of surface wave dispersion

2017 ◽  
Author(s):  
Robert E. Kayen ◽  
Brad A. Carkin ◽  
Skye C. Corbett
Keyword(s):  
Spectral Analysis ◽  
Surface Wave ◽  
Seismic Velocity ◽  
Strong Motion ◽  
Wave Dispersion ◽  
Site Characterization ◽  
Surface Wave Dispersion ◽  
Strong Motion Recording

Site Characterization of Italian Strong Motion Recording Stations

2008 ◽  
Author(s):  
Giuseppe Scasserra ◽  
Jonathan P. Stewart ◽  
Robert E. Kayen ◽  
Giuseppe Lanzo ◽  
Adolfo Santini ◽  
...  
Keyword(s):  
Strong Motion ◽  
Site Characterization ◽  
Strong Motion Recording

scholarly journals 2D characterization of near‐surface : surface‐wave dispersion inversion versus refraction tomography

2015 ◽  
Vol 13 (4) ◽  
pp. 315-332 ◽  
Author(s):  
Sylvain Pasquet ◽  
Ludovic Bodet ◽  
Laurent Longuevergne ◽  
Amine Dhemaied ◽  
Christian Camerlynck ◽  
...  
Keyword(s):  
Surface Wave ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Near Surface ◽  
Refraction Tomography

scholarly journals A Feasibility Study on Monitoring Crustal Structure Variations by Direct Comparison of Surface Wave Dispersion Curves from Ambient Seismic Noise

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
K. Muhumuza
Keyword(s):  
Surface Wave ◽  
Rayleigh Wave ◽  
Crustal Structure ◽  
Upper Mantle ◽  
Ambient Noise ◽  
Seismic Velocity ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Wave Group ◽  
Surface Wave Dispersion

This work assesses the feasibility of the direct use of surface-wave dispersion curves from seismic ambient noise to gain insight into the crustal structure of Bransfield Strait and detect seasonal seismic velocity changes. We cross-correlated four years of vertical component ambient noise data recorded by a seismic array in West Antarctica. To estimate fundamental mode Rayleigh wave Green’s functions, the correlations are computed in 4-hr segments, stacked over 1-year time windows and moving windows of 3 months. Rayleigh wave group dispersion curves are then measured on two spectral bands—primary (10–30 s) and secondary (5–10 s) microseisms—using frequency-time analysis. We analyze the temporal evolution of seismic velocity by comparing dispersion curves for the successive annual and 3-month correlation stacks. Our main assumption was that the Green’s functions from the cross-correlations, and thus the dispersion curves, remain invariant if the crustal structure remains unchanged. Maximum amplitudes of secondary microseisms were observed during local winter when the Southern Ocean experiences winter storms. The Rayleigh wave group velocity ranges between 2.1 and 3.7 km/s, considering our period range studied. Interannual velocity variations are not much evident. We observe a slight velocity decrease in summer and increase in winter, which could be attributed to the pressure melting of ice and an increase in ice mass, respectively. The velocity anomalies observed within the crust and upper mantle structure correlate with the major crustal and upper mantle features known from previous studies in the area. Our results demonstrate that the direct comparison of surface wave dispersion curves extracted from ambient noise might be a useful tool in monitoring crustal structure variations.

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.

Sign in / Sign up

Export Citation Format

Share Document