Estimation of shallow subsurface shear-wave velocity by inverting fundamental and higher-mode Rayleigh waves

2007 ◽  
Vol 27 (7) ◽  
pp. 599-607 ◽  
Author(s):  
Xianhai Song ◽  
Hanming Gu ◽  
Jiangping Liu ◽  
Xueqiang Zhang
Keyword(s):  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Rayleigh Waves ◽  
Shallow Subsurface

scholarly journals Delineation of spatial variation of shear wave velocity with high-frequency Rayleigh waves in Anchorage, Alaska

2000 ◽  
Vol 143 (2) ◽  
pp. 365-375 ◽  
Author(s):  
U. Dutta ◽  
N. Biswas ◽  
A. Martirosyan ◽  
S. Nath ◽  
M. Dravinski ◽  
...  
Keyword(s):  
Spatial Variation ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
High Frequency ◽  
Rayleigh Waves

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.

scholarly journals Near-surface characterization using a roadside distributed acoustic sensing array

2020 ◽  
Vol 39 (9) ◽  
pp. 646-653 ◽  
Author(s):  
Siyuan Yuan ◽  
Ariel Lellouch ◽  
Robert G. Clapp ◽  
Biondo Biondi
Keyword(s):  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Fundamental Mode ◽  
Rayleigh Waves ◽  
Low Frequency ◽  
Frequency Range ◽  
Acoustic Sensing ◽  
Velocity Inversion ◽  
Distributed Acoustic Sensing

Due to the broadband nature of distributed acoustic sensing (DAS) measurement, a roadside section of the Stanford DAS-2 array can record seismic signals from various sources. For example, it measures the earth's quasistatic deformation caused by the weight of cars (less than 0.8 Hz) as well as Rayleigh waves induced by earthquakes (less than 3 Hz) and by dynamic car-road interactions (3–20 Hz). We directly utilize the excited surface waves for shallow shear-wave velocity inversion. Rayleigh waves induced by passing cars have a consistent fundamental mode and a noisier first mode. By stacking dispersion images of 33 passing cars, we obtain stable dispersion images. The frequency range of the fundamental mode can be extended by adding the low-frequency earthquake-induced Rayleigh waves. Due to the extended frequency range, we can achieve better depth coverage and resolution for shear-wave velocity inversion. To assure clear separation from Love waves and to align apparent and true phase velocities, we choose an earthquake that is approximately in line with the array. The inverted models match those obtained by a conventional geophone survey, performed using active sources by a geotechnical service company contracted by Stanford University, from the surface to about 50 m. To automate the VS inversion process, we introduce a new objective function that avoids manual dispersion curve picking. We construct a 2D VS profile by performing independent 1D inversions at multiple locations along the fiber. From the low-frequency quasistatic deformation recordings, we also invert for a single Poisson's ratio at each location along the fiber. We observe spatial heterogeneity of both VS and Poisson's ratio profiles. Our approach is less expensive than ambient field interferometry, and reliable estimates can be obtained more frequently because no lengthy crosscorrelations are required.

scholarly journals Multi-channel analysis of surface wave method for geotechnical site characterization in Yogyakarta, Indonesia

2019 ◽  
Vol 76 ◽  
pp. 03006
Author(s):  
Nwai Le Ngal ◽  
Subagyo Pramumijoyo ◽  
Iman Satyarno ◽  
Kirbani Sri Brotopuspito ◽  
Junji Kiyono ◽  
...  
Keyword(s):  
Surface Waves ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Rayleigh Waves ◽  
Influential Factors ◽  
Multichannel Analysis ◽  
Average Shear ◽  
Channel Analysis ◽  
Masw Method

On May 27th 2006, Yogyakarta earthquake happened with 6.3 Mw. It was causing widespread destruction and loss of life and property. The average shear wave velocity to 30 m (Vs30) is useful parameter for classifying sites to predict their potential to amplify seismic shaking (Boore, 2004) [1]. Shear wave velocity is one of the most influential factors of the ground motion. The average shear wave velocity for the top 30 m of soil is referred to as Vs30. In this study, the Vs30 values were calculated by using multichannel analysis of surface waves (MASW) method. The Multichannel Analysis of Surface Waves (MASW) method was introduced by Park et al. (1999). Multi-channel Analysis of Surface Waves (MASW) is non-invasive method of estimating the shear-wave velocity profile. It utilizes the dispersive properties of Rayleigh waves for imaging the subsurface layers. MASW surveys can be divided into active and passive surveys. In active MASW method, surface waves can be easily generated by an impulsive source like a hammer, sledge hammer, weight drops, accelerated weight drops and explosive. Seismic measurements were carried out 44 locations in Yogyakarta province, in Indonesia. The dispersion data of the recorded Rayleigh waves were processed by using Seisimager software to obtain shear wave velocity profiles of the studied area. The average shear wave velocities of the soil obtained are ranging from 200 ms-1 to 988 ms-1, respectively.

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