1887

Abstract

Summary

Time-lapse resistivity surveys are used to monitor changes in the subsurface. In some situations, it is known that the resistivity will only decrease (or increase) with time. The 4-D ERT smoothness-constrained inversion method, that includes temporal smoothness constraint, has proved to be a robust method that reduces artefacts due to noise. However, in some cases, the time-lapse inverse models might show an increase in the resistivity with time where it is only expected to decrease. We modify the 4-D ERT inverse method to include a constraint that removes this artefact. The standard 4-D ERT inversion algorithm is first used to generate an initial model. If the resistivity is expected to decrease with time, for the model cells that show a resistivity increase with time, a truncation procedure is used where the resistivities of the different time models are reset to the mean value (corresponding to zero change with time). We then use the method of transformations in the inversion method that ensures the resistivities of the later time models are always less than the first model. The constraints can be modified so that they are only applied to selected regions in the model in cases where additional information is available.

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/content/papers/10.3997/2214-4609.201802625
2018-09-09
2024-03-28
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References

  1. AukenE., PellerinL., ChristensenN. B., SørensenK. I.
    , 2006, A survey of current trends in near-surface electrical and electromagnetic methods. Geophysics71, G249–G260.
    [Google Scholar]
  2. CassianiG., BrunoV., VillaA., FusiN. and BinleyA.M.
    2006. A saline trace test monitored via time-lapse surface electrical resistivity tomography. Journal of Applied Geophysics59, 244–259.
    [Google Scholar]
  3. DanielsR.W.
    1978. An introduction to numerical methods and optimization techniques. ElsevierNorth-Holland.
    [Google Scholar]
  4. deGroot-HedlinC. and ConstableS.
    1990. Occam’s inversion to generate smooth, two-dimensional models from magnetotelluric data. Geophysics55, 1613–1624.
    [Google Scholar]
  5. FarquharsonC.G. and OldenburgD.W.
    1998. Nonlinear inversion using general measures of data misfit and model structure. Geophysical Journal International134, 213–227.
    [Google Scholar]
  6. KimJ. H., YiM J., ParkS G. and KimJ.G.
    2009. 4-D inversion of DC resistivity monitoring data acquired over a dynamically changing earth model. Journal of Applied Geophysics68, 522–532.
    [Google Scholar]
  7. LokeM.H., AcworthI. and DahlinT.
    2003. A comparison of smooth and blocky inversion methods in 2D electrical imaging surveys. Exploration Geophysics34, 182–187.
    [Google Scholar]
  8. LokeM.H., ChambersJ.E., RuckerD.F., KurasO. and WilkinsonP.B.
    2013. Recent developments in the direct-current geoelectrical imaging method. Journal of Applied Geophysics95, 135–156.
    [Google Scholar]
  9. LokeM.H., DahlinT. and RuckerD.F.
    2014. Smoothness-constrained time-lapse inversion of data from 3-D resistivity surveys. Near Surface Geophysics12, 5–24.
    [Google Scholar]
  10. LokeM.H., WilkinsonP.B., ChambersJ. E. and MeldrumP.I.
    , 2018. Rapid inversion of data from 2-D resistivity surveys with electrodes displacements. Geophysical Prospecting66, 579–594.
    [Google Scholar]
  11. RosqvistH., LerouxV., DahlinT., SvenssonM., LindsjoM, ManssonC-H. and JohanssonS.
    , 2011. Mapping landfill gas migration using resistivity monitoring. Waste and Resource Management164 (WR1), 3–15.
    [Google Scholar]
  12. Rucker, D.F, Crook, N., Winterton, J., McNeill, M., Baldyga, C.A., Noonan, G. and Fink, J.B.
    2014. Real-time electrical monitoring of reagent delivery during a subsurface amendment experiment. Near Surface Geophysics12, 151–163.
    [Google Scholar]
  13. Uhlemann, S, Chambers, J, Wilkinson, P, Maurer, H, Merritt, A, Meldrum, P, Kuras, O, Gunn, D, Smith, A and Dijkstra, T.
    2017. 4D imaging of moisture dynamics during landslide reactivation. Journal of Geophysical Research: Earth Surface122(1), 398–418.
    [Google Scholar]
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