1887

Abstract

Summary

Inversion results obtained by surface-based electrical resistivity tomography (ERT) are strongly dependent on model regularisation (deterministic inversion) or the prior model (Bayesian inversion). Here, we present the first results of using a structure-based prior in Bayesian inversion of ERT data using a Markov chain Monte Carlo method. The method can handle unstructured meshes, which implies that topography and internal boundaries can be accounted for. The results obtained are greatly improved compared to those obtained by a prior based on uncorrelated model parameters. In the future, we will consider the problem of inferring the sediment-bedrock interface depth (and the associated uncertainty) under strong geological heterogeneity below and above the bedrock.

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/content/papers/10.3997/2214-4609.201702022
2017-09-03
2024-03-29
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References

  1. Chambers, J., Wilkinson, P., Wardrop, D., Hameed, A., Hill, I., Jeffrey, C, Loke, M., Meldrum, P., Kuras, O., Cave, M. and Gunn, D.
    [2012] Bedrock detection beneath river terrace deposits using three-dimensional electrical resistivity tomography. Geomorphology, 177–178, 17–25.
    [Google Scholar]
  2. Hastings, W.
    [1970] Monte Carlo sampling method using Markov chains and their applications. Biometrika, (57), 97–109.
    [Google Scholar]
  3. Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A. and Teller, E.
    [1953] Equations of state calculations by fast computing machines. Journal of Chemical Physics, (21), 1342–1358.
    [Google Scholar]
  4. de Pasquale, G. and Linde, N.
    [2017] On structure-based priors in Bayesian geophysical inversion. Geophysical Journal International, 208(3), 1342–1358.
    [Google Scholar]
  5. Rosas-Carbajal, M., Linde, N., Kalscheuer, T. and Vrugt, J.A.
    [2014] Two-dimensional probabilistic inversion of plane-wave electromagnetic data: methodology, model constraints and joint inversion with electrical resistivity data. Geophysical Journal International, 196, 1508–1524.
    [Google Scholar]
  6. Rücker, C, Günther, T. and Spitzer, K.
    [2006] Three-dimensional modelling and inversion of dc resistivity data incorporating topography-I. Modelling, II. Inversion. Geophysical Journal International, 166,495–517.
    [Google Scholar]
  7. Saas, O.
    [2007] Bedrock detection and talus thickness assessment in the European Alps using geophysical methods. Journal of Applied Geophysics, 62(3), 254–269.
    [Google Scholar]
  8. Sambridge, M. and Mosegaard, K.
    [2002] Monte Carlo methods in geophysical inversion problems. Review of Geophysics, 3(40), 1–29.
    [Google Scholar]
  9. Tarantola, A.
    [2005] Inverse Problem Theory and Model Parameter Estimation.Society for Industrial and Applied Mathematics, Philadelphia (USA).
    [Google Scholar]
  10. Tarantola, A. and Valette, B.
    [1982] Inverse problem == quest for informatoin.Geophysics, (50), 150— 170.
    [Google Scholar]
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