scholarly journals Accurate Sparse Recovery of Rayleigh Wave Characteristics Using Fast Analysis of Wave Speed (FAWS) Algorithm for Soft Soil Layers

2018 ◽  
Vol 8 (7) ◽  
pp. 1204
Author(s):  
Zhuoshi Chen ◽  
Baofeng Jiang ◽  
Jingjing Song ◽  
Wentao Wang
Keyword(s):  
Surface Wave ◽  
Phase Velocity ◽  
Dispersion Curve ◽  
Wave Speed ◽  
Soft Soil ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Fast Analysis ◽  
Soil Layers ◽  
Norm Minimization

This paper presents a novel fast analysis of wave speed (FAWS) algorithm from the waveforms recorded by a random-spaced geophone array based on a compressive sensing (CS) platform. Rayleigh-type seismic surface wave testing is excited by a hammer source and conducted to develop the phase velocity characteristics of the subsoil layers in Shenyang Metro line 9. Data are filtered by a bandpass filter bank to pursue the dispersive profiles of phase velocity at various frequencies. The Rayleigh-type surface-wave dispersion curve for the soil layers at each frequency is conducted by the ℓ1-norm minimization algorithm of CS theory. The traditional frequency-wavenumber transform technique and in-site downhole observation are employed as the comparison of the proposed technique. The experimental results indicate the proposed FAWS algorithm has a good agreement with both the results of conventional even-spaced geophone array and the in-site measurements, which provides an effective and efficient way for accurate non-destructive evaluation of the surface wave dispersion curve of the soil.

Surface Wave Dispersion Imaging Using Improved τ-p Transform Approach

2013 ◽  
Vol 353-356 ◽  
pp. 1196-1202 ◽  
Author(s):  
Jian Qi Lu ◽  
Shan You Li ◽  
Wei Li
Keyword(s):  
Surface Wave ◽  
Phase Shift ◽  
Dispersion Curve ◽  
Wave Dispersion ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Narrow Frequency Range ◽  
Channel Analysis ◽  
Rayleigh Wave Dispersion

Surface wave dispersion imaging approach is crucial for multi-channel analysis of surface wave (MASW). Because the resolution of inversed S-wave velocity and thickness of a layer are directly subjected to the resolution of imaged dispersion curve. The τ-p transform approach is an efficient and commonly used approach for Rayleigh wave dispersion curve imaging. However, the conventional τ-p transform approach was severely affected by waves amplitude. So, the energy peaks of f-v spectrum were mainly gathered in a narrow frequency range. In order to remedy this shortage, an improved τ-p transform approach was proposed by this paper. Comparison has been made between phase shift and improved τ-p transform approaches using both synthetic and in situ tested data. Result shows that the dispersion image transformed from proposed approach is superior to that either from conventionally τ-p transform or from phase shift approaches.

Estimating surface wave dispersion curve based on waveform calculation examination

2015 ◽  
Author(s):  
Yahui Yang ◽  
Hui Zhou ◽  
Yuhua Chen ◽  
Yanqi Li ◽  
Xiaofeng Zou ◽  
...  
Keyword(s):  
Surface Wave ◽  
Dispersion Curve ◽  
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.

Surface Wave Dispersion in a Layered Medium for Varying Subsurface Scenarios

2020 ◽  
Vol 11 (2) ◽  
pp. 26-49
Author(s):  
Narayan Roy ◽  
Aniket Desai ◽  
Ravi S. Jakka
Keyword(s):  
Surface Wave ◽  
Dispersion Curve ◽  
Heterogeneous Media ◽  
Numerical Study ◽  
Wave Dispersion ◽  
Dispersion Property ◽  
Surface Wave Dispersion ◽  
Different Types ◽  
Stiffness Variation ◽  
A Site

Surface wave techniques are widely used to characterize a site based on shear wave velocity (Vs) or stiffness variation with depth. It utilizes the dispersion property of Rayleigh wave in a heterogeneous media. Dispersion curve is obtained from analyzing collected field test data and the final Vs profile is extracted from the inversion of the generated dispersion curve. The varying subsoil structures influence whether one or more Rayleigh modes will participate in the resulting wave propagation phenomenon. So, neglecting the higher mode participation may sometimes results in a completely different velocity profile than the actual existing one. In this paper, a detailed and comprehensive numerical study has been performed using finite element method for different types of soil profiles with different half-space impedances to assess how it affects the surface wave dispersion phenomenon. In addition to that, the effect of different data acquisition parameters on surface wave dispersion has also been studied.

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