1887

Abstract

Summary

Geophysical inverse problems are non-unique. Through regularization and the use of a priori information we can derive stable and geologically reasonable inversion models. Providing an analysis of the model uncertainty is necessary for the critical task of separating inversion artefacts from robust geological features. In this work we explore the utility of “most-squares” analysis extended to nonlinear problems for quantifying uncertainty of models obtained by standard linearized iterative least squares methods, using 1D and 2D magnetotelluric examples to demonstrate its effectiveness.

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/content/papers/10.3997/2214-4609.201803267
2018-12-03
2024-04-25
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References

  1. ChenJ. and Hoversten, G.M.
    [2012] Joint inversion of marine seismic AVA and CSEM data using statistical rock-physics models and Markov random fields. Geophysics, 77, no. 1, R65–R80.
    [Google Scholar]
  2. Fichtner, A. and Trampert, J.
    [2011] Resolution analysis in full waveform inversion. Geophysical Journal International, 187, 1604–1624.
    [Google Scholar]
  3. Jackson, D.D.
    [1976] Most squares inversion. Journal of Geophysical Research, 81, 1027–1030.
    [Google Scholar]
  4. Kalscheuer, T. and Pedersen, L.B.
    [2007] A non-linear truncated SVD variance and resolution analysis of two-dimensional magnetotelluric models. Geophysical Journal International, 169, 435–447.
    [Google Scholar]
  5. Mackie, R.L., Miorelli, F., and Meju, M.A.
    [2018] Practical methods for model uncertainty quantification in electromagnetic inverse problems. 56th Annual International Meeting, SEG, Expanded Abstracts.
    [Google Scholar]
  6. Malinverno, A.
    [2002] Parsimonious Bayesian Markov chain Monte Carlo inversion in a nonlinear geophysical problem. Geophysical Journal International, 151, 675–688.
    [Google Scholar]
  7. Meju, M.A.
    [1994] Biased estimation: a simple framework for inversion and uncertainty analysis with prior information. Geophysical Journal International, 119, 521–528.
    [Google Scholar]
  8. [2009] Regularized extremal bounds analysis (REBA): An approach to quantifying uncertainty in nonlinear geophysical inverse problems. Geophysical Research Letters, 36, L03304.
    [Google Scholar]
  9. Meju, M.A. and Hutton, V.R.S.
    [1992] Iterative most-squares inversion: application to magnetotelluric data. Geophysical Journal International, 108, 758–766.
    [Google Scholar]
  10. Meju, M.A. and Sakkas, V.
    [2007] Heterogeneous crust and upper mantle across southern Kenya and the relationship to surface deformation as inferred from magnetotelluric imaging. Journal of Geophysical Research, 112, B04103.
    [Google Scholar]
  11. Oldenburg, D.W. and Li, Y.
    [1999] Estimating depth of investigation in dc resistivity and IP surveys. Geophysics, 64, 403–416.
    [Google Scholar]
  12. Rodi, W.L. and Mackie, R.L.
    [2001] Nonlinear conjugate gradients algorithm for 2-D magnetotelluric inversion. Geophysics, 66, 254–262.
    [Google Scholar]
  13. Sambridge, M. and Mosegaard, K.
    [2002] Monte Carlo methods in geophysical inverse problems. Reviews of Geophysics, 40, 3–1-3–29.
    [Google Scholar]
  14. Tompkins, M.J., Martinez, J.L.F., and Muniz, Z.F.
    [2011] Marine electromagnetic inverse solution appraisal and uncertainty using model-derived basis functions and sparse geometric sampling. Geophysical Prospecting, 59, 947–965.
    [Google Scholar]
  15. Whittall, K.P. and Oldenburg, D.W.
    [1992] Inversion of magnetotelluric data for a one-dimensional conductivity, Geophysical Monograph Series, No. 5, SEG.
    [Google Scholar]
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