SOLVING THE INVERSE PROBLEM OF THE MASW METHOD BASED ON A NEW BIO-INSPIRED OPTIMIZATION ALGORITHM (SSA)
1 Novosibirsk State University
2 Trofimuk Institute of Petroleum Geology and Geophysics SB RAS
3 Chinakal Institute of Mining SB RAS
Journal: Geophysical research
Tome: 25
Number: 3
Year: 2024
Pages: 5-28
UDK: 550.34.01
DOI: 10.21455/gr2024.3-1
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Yablokov
R.A A.V. SOLVING THE INVERSE PROBLEM OF THE MASW METHOD BASED ON A NEW BIO-INSPIRED OPTIMIZATION ALGORITHM (SSA) // . 2024. Т. 25. № 3. С. 5-28. DOI: 10.21455/gr2024.3-1
@article{Yablokov
R.ASOLVING2024,
author = "Yablokov
R.A, A. V.",
title = "SOLVING THE INVERSE PROBLEM OF THE MASW METHOD BASED ON A NEW BIO-INSPIRED OPTIMIZATION ALGORITHM (SSA)",
journal = "Geophysical research",
year = 2024,
volume = "25",
number = "3",
pages = "5-28",
doi = "10.21455/gr2024.3-1",
language = "English"
}
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Keywords: MASW, inversion, SSA, swarm method, engineering seismic exploration.
Аnnotation: A new approach to solving the inverse problem of the MASW (Multichannel Analysis of Surface Waves) meth-od – determining one-dimensional models of shear wave velocity by inverting surface wave dispersion curves – is proposed based on a bio-inspired Salp Swarm Algorithm (SSA) optimization algorithm. Its idea is to simulate the movement of search agents (salps) in the space of reconstructed parameters similar to the search for food in order to find a global optimum. The initial positions of salps and the food source are randomly initialized within specified boundaries. During the iteration process, salps move towards the food source that is considered the best solution for the current iteration. The optimization process involves updating the positions of salps based on mathematical expressions and constraints of the search space. The algorithm also adaptively
adjusts the coefficient that determines the balance between the stages of exploring the entire space and using
local optima.
In this study, SSA is used to solve the problem of inversion of dispersion curves of phase velocities of sur-face waves. In this formulation, the food source is the position of the values of the transverse wave velocity vectors and the power of the layers of the reconstructed velocity model in the multidimensional space of the re-sidual functional, determined by the selected metric. A clear visualization of the SSA operation is presented us-ing an example of the two-dimensional optimization problem, and its effectiveness in finding global optima is demonstrated.
Various metrics of the residual functional are considered, which play an important role in assessing the accuracy of the solution. Two groups of metrics with different accuracy are identified and their applicability in SSA is analyzed. Studying the position history of search agents and convergence curves shows that productivity of the algorithm increases with iteration number increasing, and it is also able to effectively master the solution space and avoid local optima.
The results of synthetic experiments are analyzed, including various classes of layered velocity models. The results of using SSA and the Gray Wolf Optimizer (GWO) algorithm are compared. Both algorithms give good results, but SSA provides a more stable solution, in addition, it is successfully used in a overparameterized formulation and demonstrates higher efficiency compared to the GWO algorithm with reasonable restrictions on the solution search space.
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Dunkin J.W., Computation of modal solutions in layered, elastic media at high frequencies, Bulletin of the Seismological Society of America, 1965, vol. 55, no. 2, pp. 335-358.
Foti S., Lai C.G., Rix G.J., Strobbia C., Surface wave methods for near-surface site characterization, London, CRC Press, 2014, 487 p.
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Miller R.D., Xia J., Park C.B., Ivanov J., Williams E., Using MASW to map bedrock in Olathe, Kansas, in SEG Technical Program Expanded Abstracts 1999, Houston, TX, USA, Society of Exploration Geophysicists, 1999, pp. 433-436. https://doi.org/10.1190/1.1821045
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Socco L.V., Foti S., Boiero D., Surface-wave analysis for building near-surface velocity models-established approaches and new perspectives, Geophysics, 2010, vol. 75, no. 5, pp. A83-A102.
Song X., Tang L., Zhao S., Zhang X., Li L., Huang J., Cai W., Grey Wolf Optimizer for parameter estimation in surface waves, Soil Dynamics and Earthquake Engineering, 2015, vol. 75, pp. 147-157.
Wolpert D.H., Macready W.G., No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, 1997, vol. 1, no. 1, pp. 67-82.
Xia J., Miller R.D., Park C.B., Estimation of near-surface shear-wave velocity by inversion of Rayleigh waves, Geophysics, 1999, vol. 64, no. 3, pp. 691-700.
Xia J., Estimation of near-surface shear-wave velocities and quality factors using multichannel analysis of sur-face-wave methods, Journal of Applied Geophysics, 2014, vol. 103, pp. 140-151.
Yablokov A., Lugovtsova Y., Serdyukov A., Uncertainty quantification of multimodal surface wave inversion using artificial neural networks, Geophysics, 2023, vol. 88, no. 2, pp. KS1-KS11.
Yablokov A.V., Serdyukov A.S., Loginov G.N., Baranov V.D., An artificial neural network approach for the in-version of surface wave dispersion curves, Geophysical Prospecting, 2021, vol. 69, no. 7, pp. 1405-1432.
Dal Moro G., Pipan M., Gabrielli P., Rayleigh wave dispersion curve inversion via genetic algorithms and mar-ginal posterior probability density estimation, Journal of Applied Geophysics, 2007, vol. 61, no. 1, pp. 39-55. https://doi.org/10.1016/j.jappgeo.2006.04.002
Dunkin J.W., Computation of modal solutions in layered, elastic media at high frequencies, Bulletin of the Seismological Society of America, 1965, vol. 55, no. 2, pp. 335-358.
Foti S., Lai C.G., Rix G.J., Strobbia C., Surface wave methods for near-surface site characterization, London, CRC Press, 2014, 487 p.
Kurita T., Regional variations in the structure of the crust in the central United States from P-wave spectra, Bulletin of the Seismological Society of America, 1973, vol. 63, no. 5, pp. 1663-1687.
Lai C.G., Foti S., Rix G.J., Propagation of data uncertainty in surface wave inversion, Journal of Environmental and Engineering Geophysics, 2005, vol. 10, no. 2, pp. 219-228.
Lin C.P., Chang C.C., Chang T.S., The use of MASW method in the assessment of soil liquefaction potential, Soil Dynamics and Earthquake Engineering, 2004, vol. 24, no. 9-10, pp. 689-698.
Miller R.D., Xia J., Park C.B., Ivanov J., Williams E., Using MASW to map bedrock in Olathe, Kansas, in SEG Technical Program Expanded Abstracts 1999, Houston, TX, USA, Society of Exploration Geophysicists, 1999, pp. 433-436. https://doi.org/10.1190/1.1821045
Mirjalili S., Gandomi A.H., Mirjalili S.Z., Saremi S., Faris H., Mirjalili S.M., Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems, Advances in Engineering Software, 2017, vol. 114, pp. 163-191.
Pei D., Louie J.N., Pullammanappallil S.K., Application of simulated annealing inversion on high-frequency fundamental-mode Rayleigh wave dispersion curves, Geophysics, 2007, vol. 72, no. 5, pp. R77-R85.
Ryden N., Park C.B., Fast simulated annealing inversion of surface waves on pavement using phase-velocity spectra, Geophysics, 2006, vol. 71, no. 4, pp. R49-R58.
Socco L.V., Boiero D., Improved Monte Carlo inversion of surface wave data, Geophysical Prospecting, 2008, vol. 56, no. 3, pp. 357-371.
Socco L.V., Foti S., Boiero D., Surface-wave analysis for building near-surface velocity models-established approaches and new perspectives, Geophysics, 2010, vol. 75, no. 5, pp. A83-A102.
Song X., Tang L., Zhao S., Zhang X., Li L., Huang J., Cai W., Grey Wolf Optimizer for parameter estimation in surface waves, Soil Dynamics and Earthquake Engineering, 2015, vol. 75, pp. 147-157.
Wolpert D.H., Macready W.G., No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, 1997, vol. 1, no. 1, pp. 67-82.
Xia J., Miller R.D., Park C.B., Estimation of near-surface shear-wave velocity by inversion of Rayleigh waves, Geophysics, 1999, vol. 64, no. 3, pp. 691-700.
Xia J., Estimation of near-surface shear-wave velocities and quality factors using multichannel analysis of sur-face-wave methods, Journal of Applied Geophysics, 2014, vol. 103, pp. 140-151.
Yablokov A., Lugovtsova Y., Serdyukov A., Uncertainty quantification of multimodal surface wave inversion using artificial neural networks, Geophysics, 2023, vol. 88, no. 2, pp. KS1-KS11.
Yablokov A.V., Serdyukov A.S., Loginov G.N., Baranov V.D., An artificial neural network approach for the in-version of surface wave dispersion curves, Geophysical Prospecting, 2021, vol. 69, no. 7, pp. 1405-1432.