1887

Abstract

Summary

Deterministic full-waveform inversion will converge towards a local minimum if the forward-simulated data based on the starting model differ from the observed data by more than half a wavelength. In order to include prior information and derive posterior probability density functions that are independent of the starting model, we use a global sampling method. We consider a synthetic multi-Gaussian test case and use an efficient adaptive Markov chain Monte Carlo (MCMC) method in combination with dimensionality reduction through circulant embedding to invert the noise-contaminated data. By considering multiple MCMC runs with different starting models, we find that we always recover model realizations with appropriate data misfits, but that different runs lead to different estimated posterior distributions. This is a consequence of the severe non-linearity of the full-waveform inversion problem and the finite length of our MCMC chains. Nevertheless, the different runs give a good idea of the types of subsurface models that are consistent with the data.

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/content/papers/10.3997/2214-4609.201702113
2017-09-03
2024-03-29
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References

  1. ter Braak, C.J.F. and Vrugt, J.A.
    [2008] Differential Evolution Markov Chain with snooker updater and fewer chains. Statistics and Computing, 18(4), 435–446.
    [Google Scholar]
  2. Bunks, C., Saleck, F.M., Zaleski, S. and Chavent, G.
    [1995] Multiscale seismic waveform inversion. Geophysics, 60(5), 1457–1473.
    [Google Scholar]
  3. Cordua, K.S., Hansen, T.M. and Mosegaard, K.
    [2012] Monte Carlo full-waveform inversion of cross-hole GPR data using multiple-point geostatistical a priori information. Geophysics, 77, H19–H31.
    [Google Scholar]
  4. Dietrich, C.R. and Newsam, G.N.
    [1997] Fast and exact simulation of stationary Gaussian processes through circulant embedding of the covariance matrix. SIAM Journal of Scientific Computation, 18, 1088–1107.
    [Google Scholar]
  5. Ernst, J.R., Green, A.G., Maurer, H. and Holliger, K.
    [2007] Application of a new 2D time-domain full-waveform inversion scheme to crosshole radar data. Geophysics, 72, J53–J64.
    [Google Scholar]
  6. Klotzsche, A., van der Kruk, J., Bradford, J. and Vereecken, H.
    [2014] Detection of spatially limited high-porosity layers using crosshole GPR signal analysis and full-waveform inversion. Water Resources Research, 50, 6966–6985.
    [Google Scholar]
  7. Laloy, E., Linde, N., Jacques, D. and Vrugt, J.A.
    [2015] Probabilistic inference of multi-Gaussian fields from indirect hydrological data using circulant embedding and dimensionality reduction. Water Resources Research, 51, 4224–4243.
    [Google Scholar]
  8. Laloy, E. and Vrugt, J.A.
    [2012] High-dimensional posterior exploration of hydrologic models using multiple-try DREAM(ZS) and high-performance computing. Water Resources Research, 48, WO1526.
    [Google Scholar]
  9. Meles, G., Greenhalgh, S., van der Kruk, J., Green, A. and Maurer, H.
    [2012] Taming the non-linearity problem in {GPR} full-waveform inversion for high contrast media. Journal of Applied Geophysics, 78, 31–43.
    [Google Scholar]
  10. Virieux, J. and Operto, S.
    [2009] An overview of full-waveform inversion in exploration geophysics. Geophysics, 74(6), WCC1–WCC26.
    [Google Scholar]
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