1887

Abstract

Summary

Inversion of surface wave dispersion curves is widely used to characterize the near surface. As the inverse problem is highly non-linear, robust and efficient global optimization strategies may be best suited to find the global minimum. Here, we introduce the Grey Wolf Optimization (GWO), a swarm intelligence technique for the inversion of Rayleigh-wave phase and group velocity dispersion curves. The performance of GWO in retrieving the S-wave velocity is illustrated on a near-surface model and the results have been compared to those obtained from a perturbation approach based on finite-element modelling.

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/content/papers/10.3997/2214-4609.201901454
2019-06-03
2020-01-22
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References

  1. Agarwal, A., ans S Shalivahan, A.C. and Singh, R.K.
    [2018] Grey wolf optimizer: a new strategy to invert geophysical data sets. Geophysical Prospecting, 66(6), 1215–1226.
    [Google Scholar]
  2. Aki, K. and Richards, P.G.
    [1980] Quantitative Seismology. W. H. Freeman and Company.
    [Google Scholar]
  3. Cercato, M.
    [2007] Computation of partial derivatives of Rayleigh-wave phase velocity using second-order subdeterminants. Geophysical Journal International, 170, 217–238.
    [Google Scholar]
  4. [2008] Addressing non-uniqueness in linearized multichannel surface wave inversion. Geophysical Prospecting, 57, 27–47.
    [Google Scholar]
  5. Haney, M.M. and Tsai, V.C.
    [2015] Nonperturbational surface-wave inversion: A Dix-type relation for surface waves. Geophysics, 80(6), EN167–EN177.
    [Google Scholar]
  6. [2017] Perturbational and nonperturbational inversion of Rayleigh-wave velocities. Geophysics, 82(3), F15–F28.
    [Google Scholar]
  7. Mirjalili, S., Mirjalili, S.M. and Lewis, A.
    [2014] Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61.
    [Google Scholar]
  8. Moro, G.D., Pipan, M. and Gabrielli, P.
    [2007] Raleigh wave dispersion curve inversion via genetic algorithms and Marginal Posterior Probability Density estimation. Journal of Applied Geophysics, 61(1), 39–55.
    [Google Scholar]
  9. Pasquet, S. and Bodet, L.
    [2017] SWIP: An integrated workflow for surface-wave dispersion inversion and profiling. Geophysics, 82(6), WB47–WB61.
    [Google Scholar]
  10. Pei, D., louie, J.N. and Pullammanappallil, S.K.
    [2007] Application of simulated annealing inversion on high-frequency fundamental-mode Rayleigh wave dispersion curves. Geophysics, 72(5), R77–R85.
    [Google Scholar]
  11. Socco, L.V. and Boiero, D.
    [2008] Improved Monte Carlo inversion of surface wave data. Geophysical Prospecting, 56(3), 357–371.
    [Google Scholar]
  12. Song, X., Tang, L., Zhao, S., Zhang, X., Li, L., Huang, J. and Cai, W.
    [2015] Grey Wolf Optimizer for parameter estimation in surface waves. Soil Dynamics and Earthquake Engineering, 75, 147–157.
    [Google Scholar]
  13. Xia, J., Miller, R.D. and park, C.B.
    [1999] Estimation of near-surface shear-wave velocity by inversion of Rayleigh waves. Geophysics, 64, 691–700.
    [Google Scholar]
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