scholarly journals Velocity modeling based on Rayleigh wave dispersion curve and sparse optimization inversion

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Yan Cui ◽  
Yanfei Wang
Keyword(s):  
Rayleigh Wave ◽  
Dispersion Curve ◽  
Wave Dispersion ◽  
Sparse Optimization ◽  
Velocity Modeling ◽  
Rayleigh Wave Dispersion

Rayleigh Wave Dispersion Curve Inversion with the Artificial Bee Colony Algorithm

2021 ◽  
Vol 26 (2) ◽  
pp. 99-110
Author(s):  
Xin Wang ◽  
Hongyan Shen ◽  
Xinxin Li ◽  
Qin Li ◽  
Daoyuan Wang
Keyword(s):  
Rayleigh Wave ◽  
Dispersion Curve ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Wave Dispersion ◽  
Velocity Model ◽  
S Wave ◽  
Bee Colony ◽  
Search Optimization ◽  
Rayleigh Wave Dispersion

Rayleigh wave dispersion curve inversion is a non-linear iterative optimization process with multi-parameter and multi-extrema. It is difficult to carry out inversion and reconstruction of stratigraphic parameters quickly and accurately with a single linear or non-linear inversion for the data processing of Rayleigh waves with complex seismic geological conditions. We proposed a new method that combines artificial bee colony algorithm (ABC) and damped least squares algorithm (DLS) to invert Rayleigh wave dispersion curve. First, food sources are initialized in a large scale of the model based on the prior geological information. Then, after three kinds of bee operators (employed bees, onlooker bees and scout bees) transform each other and perform search optimization with several iterations, the targets are converged near the optimal solution to obtain an initial S-wave velocity model. Finally, the final S-wave velocity model is obtained by local optimization of DLS inversion with fast convergence and strong stability. The correctness of the method has been verified by one high-velocity interlayer model, and it was further applied to a real Rayleigh wave dataset. The results show that our method not only absorbs the advantages of ABC global search optimization and strong adaptability, but also makes full use of the advantages of DLS inversion, such as high accuracy and fast convergence speed. The inversion strategy can effectively suppress the inversion falling into local extrema, get rid of the dependence on an initial model, enhance the inversion stability, further improve the convergence speed and inversion accuracy, while has good anti-noise ability.

scholarly journals Estimation of <i>Vs</i> profile using its natural frequency and Rayleigh-wave dispersion characteristics

2008 ◽  
Vol 14 ◽  
pp. 75-77 ◽  
Author(s):  
K. Tokeshi ◽  
M. Karkee ◽  
C. Cuadra
Keyword(s):  
Rayleigh Wave ◽  
Dispersion Curve ◽  
Natural Frequency ◽  
Numerical Test ◽  
Wave Dispersion ◽  
Test Site ◽  
Dispersion Characteristics ◽  
Rayleigh Wave Dispersion

Abstract. The evaluation of the natural frequency of random Vs profiles before analyzing the fundamental Rayleigh-wave dispersion characteristics is proposed in this paper. The inclusion of this parameter optimizes the effectiveness of random inverse searching to estimate Vs profiles. To demonstrate this method, a numerical test was performed using the "experimental" Rayleigh-wave dispersion curve obtained for a fictitious TEST site.

Use of peaks and troughs in the horizontal-to-vertical spectral ratio of ambient noise for Rayleigh-wave dispersion curve picking

2020 ◽  
Vol 177 ◽  
pp. 104024 ◽  
Author(s):  
José Piña-Flores ◽  
Martín Cárdenas-Soto ◽  
Antonio García-Jerez ◽  
Helena Seivane ◽  
Francisco Luzón ◽  
...  
Keyword(s):  
Rayleigh Wave ◽  
Dispersion Curve ◽  
Ambient Noise ◽  
Spectral Ratio ◽  
Wave Dispersion ◽  
Rayleigh Wave Dispersion

Influence of parameterization on inversion of surface wave dispersion curves and definition of an inversion strategy for sites with a strong VS contrast

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. B197-B209 ◽  
Author(s):  
F. Renalier ◽  
D. Jongmans ◽  
A. Savvaidis ◽  
M. Wathelet ◽  
B. Endrun ◽  
...  
Keyword(s):  
Rayleigh Wave ◽  
Dispersion Curve ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Linear Velocity ◽  
Frequency Range ◽  
Number Of Layers ◽  
Bedrock Depth ◽  
Definition Of ◽  
Rayleigh Wave Dispersion

Inversion of the fundamental mode of the Rayleigh wave dispersion curve does not provide a unique solution and the choice of the parameterization (number of layers, range of velocity, and thickness values for the layers) is of prime importance for obtaining reliable results. We analyzed shear-wave velocity profiles derived from borehole tests at 10 sites where soil layers overlay bedrock in various geologic contexts. One to three seismic layers with linear velocity laws could model all of them. Three synthetic models defined from this preliminary study were used to understand the influence of parameterization on the dispersion curve inversion. This analysis resulted in the definition of a two-step inversion procedure for sites exhibiting a strong impedance con-trast. In the first step, the dispersion curve is inverted with an increasing number of layers over half space. The evolution of the minimum misfit and bedrock depth with number of layers allows the estimation of the true bedrock depth range. In the second step, this information is introduced in inversions with linear velocity laws. Synthetic tests showed that applying this procedure requires the dispersion curve over a frequency range from [Formula: see text] to [Formula: see text], where [Formula: see text] is the site resonance frequency. The strategy was tested on two real cases for which Rayleigh wave dispersion curves were measured over this frequency range using passive and active seismic methods. The strategy was successful at the first site, while the bedrock depth was overestimated by 15% at the second site, probably resulting from the existence of a higher mode affecting the dispersion curve at low frequency.

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