1887

Abstract

Summary

We implement a transdimensional Bayesian algorithm to invert Rayleigh wave dispersion curves considering as unknowns the number of model parameters, that is the locations of the layer boundaries together with the shear wave velocity and the Vp/Vs ratio of each layer. A reversible jump Markov Chain Monte Carlo (MCMC) algorithm is used to sample the variable-dimension model space, while the adoption of a parallel tempering strategy and of a delayed rejection updating scheme improve the efficiency of the probabilistic sampling. This work has a mainly theoretical perspective and is aimed at drawing general conclusions about the suitability of our approach for dispersion curve inversion. For this reason, we focus on synthetic data inversions, and we limit to consider the fundamental mode, which is analytically computed from schematic 1D reference models. Our tests prove that the implemented inversion algorithm provides a parsimonious solution and successfully estimates model uncertainty and model dimensionality. In particular, as expected, the posterior uncertainties increase passing from Vs, to layer thicknesses and to Vp/Vs ratio, and changes according to the expected resolution on model parameters.

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/content/papers/10.3997/2214-4609.201902475
2019-09-08
2024-03-28
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References

  1. Bodin, T., and Sambridge, M.
    (2009). Seismic tomography with the reversible jump algorithm. Geophysical Journal International, 178(3), 1411–1436.
    [Google Scholar]
  2. Bodin, T., Sambridge, M., Tkalcic, H., Arroucau, P., Gallagher, K., and Rawlinson, N.
    (2012). Transdimensional inversion of receiver functions and surface wave dispersion. Journal of Geophysical Research: Solid Earth, 117, B02301.
    [Google Scholar]
  3. Mandolesi, E., Ogaya, X., Campanyà, J., and Agostinetti, N. P.
    (2018). A reversible-jump Markov chain Monte Carlo algorithm for 1D inversion of magnetotelluric data. Computers & Geosciences, 113, 94–105.
    [Google Scholar]
  4. Sambridge, M.
    (2014). A parallel tempering algorithm for probabilistic sampling and multimodal optimization. Geophysical Journal International, 196(1), 357–374.
    [Google Scholar]
  5. Sen, M. K., and Biswas, R.
    (2017). Transdimensional seismic inversion using the reversible jump Hamiltonian Monte Carlo algorithm. Geophysics, 82(3), R119–R134.
    [Google Scholar]
  6. Groos, L., Schäfer, M., Forbriger, T., and Bohlen, T.
    (2017). Application of a complete workflow for 2D elastic full-waveform inversion to recorded shallow-seismic Rayleigh waves. Geophysics, 82(2), R109–R117.
    [Google Scholar]
  7. SoccoL.V. and StrobbiaC.
    (2004). Surface-wave method for nearsurface characterization: a tutorial. Near Surface Geophysics2, 165–185.
    [Google Scholar]
  8. Xing, Z., and Mazzotti, A.
    (2018). Two-grid genetic algorithm full waveform inversion of surface waves: two actual data examples. In 80th EAGE Conference and Exhibition 2018.
    [Google Scholar]
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