1887
Volume 19, Issue 3
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

ABSTRACT

The combination of geophysical surface‐based imaging techniques, including seismic and electrical resistivity tomography (ERT), is now common practice to obtain a more accurate characterization of subsurface structures. Due to model non‐uniqueness and geological heterogeneity, conventional travel‐time tomography cannot solely reveal hidden layers (i.e., low‐velocity zones embedded between layers of higher velocities) in the subsurface. Hence, we present a joint inversion algorithm based on a normalized cross‐gradients function to detect hidden low‐velocity layers. The structural similarity between P‐wave velocity () and resistivity fields is enhanced by incorporating the normalized cross‐gradients constraint in the joint inversion algorithm. Improved structural similarity can mitigate the problem of the recovery of a hidden layer. We also take advantage of information derived from borehole geological data to reduce the continuous range of possible solutions (i.e., exact‐data non‐uniqueness). In both joint and separate inversions, an auxiliary damping factor is used to ensure convergence, and also the smoothness constraints are applied to deal with instability stemming from error in the data. To verify the performance of the joint inversion procedure, the algorithm is tested on synthetic and real data examples with emphasis on hidden low‐velocity layer detection. Numerical experiments demonstrate that the joint inversion strategy can produce more reliable and better velocity models of the subsurface structures as compared with those obtained through individual inversions. We conclude that this simultaneous joint inversion of and ERT integrates the best of both schemes and makes it possible to improve resolution, and, hence, reduces uncertainties in hidden low‐velocity layer problems.

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2021-05-11
2024-04-24
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References

  1. Aster, R.C., Borchers, B. and Thurber, C.H. (2011) Parameter Estimation and Inverse Problems, 2nd edition. New York: Academic.
    [Google Scholar]
  2. Backus, G.E. and Gilbert, J.F. (1970) Uniqueness in the inversion of inaccurate gross earth data. Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 266, 123–192.
    [Google Scholar]
  3. Bennington, N.L., Zhang, H., Thurber, C.H. and Bedrosian, P.A. (2015) Joint inversion of seismic and magnetotelluric data in the Parkfield region of California using the normalized cross‐gradients constraint. Pure and Applied Geophysics, 172, 1033–1052.
    [Google Scholar]
  4. Cardiff, M. and Kitanidis, P.K. (2009) Bayesian inversion for facies detection: An extensible level set framework. Water Resources Research, 45 (10), W10416.
    [Google Scholar]
  5. Ghanati, R., Hafizi, M.K., Mahmoudvand, R. and Fallahsafari, M. (2016) Filtering and parameter estimation of surface‐NMR data using singular spectrum analysis. Journal of Applied Geophysics, 130, 118–130.
    [Google Scholar]
  6. Ghanati, R., Ghari, H. and Fatehi, M. (2017) Regularized nonlinear inversion of magnetic anomalies of simple geometric models using Occan's method: An application to the Morvarid iron‐apatite deposit in Iran. Acta Geodaetica et Geophysica, 52, 555–580.
    [Google Scholar]
  7. Gallardo, L.A., (2007) Multiple cross‐gradient joint inversion for geospectral imaging. Geophysical Research Letters, 34, 1–5.
    [Google Scholar]
  8. Gallardo, L.A. and Meju, M.A. (2003) Characterization of heterogeneous near‐surface materials by joint 2D inversion of dc resistivity and seismic data. Geophysical Research Letters, 30, 1658.
    [Google Scholar]
  9. Gallardo, L.A. and Meju, M.A. (2004) Joint two‐dimensional DC resistivity and seismic travel time inversion with cross‐gradients constraints. Journal of Geophysical Research Solid Earth, 109(B3), 1–11.
    [Google Scholar]
  10. Gallardo, L.A. and Meju, M.A. (2007) Joint two‐dimensional cross‐gradient imaging of magnetotelluric and seismic travel‐time data for structural and lithological classification. Geophysical Journal International, 169, 1261–1272.
    [Google Scholar]
  11. Gallardo, L.A. and Meju, M.A. (2011) Structure‐coupled multiphysics imaging in geophysical sciences. Reviews of Geophysics, 49, 1–19.
    [Google Scholar]
  12. Gallardo, L.A., Fontes, S.L., Meju, M.A., Bounora, M.P. and De Lugao, P.P. (2012) Robust geophysical integration through structure‐coupled joint inversion and multispectral fusion of seismic reflection, magnetotelluric, magnetic, and gravity images: Example from Santos Basin, offshore Brazil. Geophysics, 77, B237–B251.
    [Google Scholar]
  13. Gao, J. and Zhang, H. (2018) An efficient sequential strategy for realizing cross‐gradient joint inversion: Method and its application to 2‐D cross borehole seismic traveltime and DC resistivity tomography. Geophysical Journal International, 213, 1044–1055.
    [Google Scholar]
  14. Haber, E. and Gazit, M.H. (2013) Model fusion and joint inversion. Surveys in Geophysics, 34, 675–695.
    [Google Scholar]
  15. Haber, E. and Oldenburg, D.W. (1997) Joint inversion: A structural approach. Inverse Problems, 13, 63–77.
    [Google Scholar]
  16. Ivanov, J., Miller, R.D., Xia, J., Steeples, D. and Park, C.B. (2005a) The inverse problem of refraction travel times, Part I: Types of geophysical nonuniqueness through minimization. Pure and Applied Geophysics, 162, 447–459.
    [Google Scholar]
  17. Ivanov, J., Miller, R.D., Xia, J., Steeples, D. and Park, C.B. (2005b) The inverse problem of refraction travel times, Part II: Quantifying refraction nonuniqueness using a three‐layer model. Pure and Applied Geophysics, 162, 461–477.
    [Google Scholar]
  18. Julia, J., Ammon, C.J., Herrmann, R.B. and Correig, A.M. (2000) Joint inversion of receiver function and surface wave dispersion observations. Geophysical Journal International, 143, 99–112.
    [Google Scholar]
  19. Lelièvre, P.G. and Oldenburg, D.W. (2009) A comprehensive study of including structural orientation information in geophysical inversions. Geophysical Journal International, 178, 623–637.
    [Google Scholar]
  20. Linde, N., Binley, A., Tryggvason, A., Pedersen, L.B. and Revil, A. (2006) Improved hydrogeophysical characterization using joint inversion of cross‐hole electrical resistance and ground penetrating radar travel time data. Water Resource Research, 42, W04410.
    [Google Scholar]
  21. Linde, N. and Doetsch, J. (2016) Joint inversion in hydro‐geophysics and near‐surface geophysics. In: Moorkamp, M., Lelievre, P., Linde, N. and Khan,A. (Eds.) Integrated Imaging of the Earth (Vol. 7, pp. 119–135). Hoboken, NJ: John Wiley & Sons.
    [Google Scholar]
  22. Linde, N., Tryggvason, A., Peterson, J.E. and Hubbard, S.S. (2008) Joint inversion of crosshole radar and seismic travel times acquired at the South Oyster Bacterial Transport Site. Geophysics, 73, G29–G37.
    [Google Scholar]
  23. Meju, M.A., Mackie, R.L., Miorelli, F., Saleh, A.S. and Miller, R.V. (2019) Structurally tailored 3D anisotropic controlled‐source electromagnetic resistivity inversion with cross‐gradient criterion and simultaneous model calibration. Geophysics, 84, E387–E402.
    [Google Scholar]
  24. Molodtsov, D.M., Troyan, V.N., Roslov, Y.V. and Zerilli, A. (2013) Joint inversion of seismic travel times and magnetotelluric data with a directed structural constraint. Geophysical Prospecting, 61, 1218–1228.
    [Google Scholar]
  25. Perez‐Flores, M.A., Mendez‐Delgado, S. and Gomez‐Trevino, E. (2001) Imaging low‐frequency and DC electromagnetic fields using a simple linear approximation. Geophysics, 66, 1067–1081.
    [Google Scholar]
  26. von Ketelhodt, J.K., Fechner, T., Manzi, M.S. and Durrheim, R.J. (2018) Joint inversion of cross‐borehole P‐waves, horizontally and vertically polarized S‐waves: Tomographic data for hydro‐geophysical site characterization. Near Surface Geophysics, 16, 529–542.
    [Google Scholar]
  27. Wang, K.P., Tan, H.D. and Wang, T. (2017) 2D joint inversion of CSAMT and magnetic data based on cross‐gradient theory. Applied Geophysics, 14, 279–290.
    [Google Scholar]
  28. Zelt, C.A. and Barton, P.J. (1998) Three‐dimensional seismic refraction tomography: A comparison of two methods applied to data from the Faeroe Basin. Journal of Geophysical Research: Solid Earth, 103, 7187–7210.
    [Google Scholar]
  29. Zhang, J. and Morgan, F.D. (1996) Joint seismic and electrical tomography. In Symposium on the Application of Geophysics to Engineering and Environmental Problems 1996 (pp. 391–396). Society of Exploration Geophysicists.
    [Google Scholar]
  30. Zhdanov, M.S. (2002) Geophysical Inverse Theory and Regularization Problems (Vol. 36). Amsterdam; Oxford: Elsevier.
    [Google Scholar]
  31. Zhou, J., Meng, X., Guo, L. and Zhang, S. (2015) Three‐dimensional cross‐gradient joint inversion of gravity and normalized magnetic source strength data in the presence of remanent magnetization. Journal of Applied Geophysics, 119, 51–60.
    [Google Scholar]
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