Joint Inversion of Rayleigh-wave Dispersion Curve for Near-Surface S-Wave Velocity Estimation

2018 ◽  
Author(s):  
A. Rubaiyn ◽  
J. Safani ◽  
A. Priyono
Keyword(s):  
Rayleigh Wave ◽  
Dispersion Curve ◽  
Wave Velocity ◽  
Joint Inversion ◽  
Wave Dispersion ◽  
Velocity Estimation ◽  
S Wave ◽  
Near Surface ◽  
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.

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 Isolating retrograde and prograde Rayleigh-wave modes using a polarity mute

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. V379-V385 ◽  
Author(s):  
Gabriel Gribler ◽  
Lee M. Liberty ◽  
T. Dylan Mikesell ◽  
Paul Michaels
Keyword(s):  
Rayleigh Wave ◽  
Wave Velocity ◽  
Frequency Dispersion ◽  
Wave Dispersion ◽  
Velocity Estimation ◽  
Joint Analysis ◽  
S Wave ◽  
Retrograde Motion ◽  
Mode Dispersion ◽  
Dispersion Data

Estimates of S-wave velocity with depth from Rayleigh-wave dispersion data are limited by the accuracy of fundamental and/or higher mode signal identification. In many scenarios, the fundamental mode propagates in retrograde motion, whereas higher modes propagate in prograde motion. This difference in particle motion (or polarity) can be used by joint analysis of vertical and horizontal inline recordings. We have developed a novel method that isolates modes by separating prograde and retrograde motions; we call this a polarity mute. Applying this polarity mute prior to traditional multichannel analysis of surface wave (MASW) analysis improves phase velocity estimation for fundamental and higher mode dispersion. This approach, in turn, should lead to improvement of S-wave velocity estimates with depth. With two simple models and a field example, we have highlighted the complexity of the Rayleigh-wave particle motions and determined improved MASW dispersion images using the polarity mute. Our results show that we can separate prograde and retrograde signals to independently process fundamental and higher mode signals, in turn allowing us to identify lower frequency dispersion when compared with single component data. These examples demonstrate that the polarity mute approach can improve estimates of S-wave velocities with depth.

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