1887
Volume 16, Issue 4
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604
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Abstract

ABSTRACT

The induced polarization phenomenon, both in time domain and frequency domain, is often parameterised using the empirical Cole–Cole model. To improve the resolution of model parameters and to decrease the parameter correlations in the inversion process of induced polarization data, we suggest here three re‐parameterisations of the Cole–Cole model, namely the maximum phase angle Cole–Cole model, the maximum imaginary conductivity Cole–Cole model, and the minimum imaginary resistivity Cole–Cole model. The maximum phase angle Cole–Cole model uses the maximum phase and the inverse of the phase peak frequency, τ, instead of the intrinsic charge‐ability and the time constant adopted in the classic Cole–Cole model. The maximum imaginary conductivity Cole–Cole model uses the maximum imaginary conductivity instead of and the time constant τ of the Cole–Cole model in its conductivity form. The minimum imaginary resistivity Cole–Cole model uses the minimum imaginary resistivity instead of and the time constant τ of the Cole–Cole model in its resistivity form.

The effects of the three re‐parameterisations have been tested on synthetic time‐domain and frequency‐domain data using a Markov chain Monte Carlo inversion method, which allows for easy quantification of parameter uncertainty, and on field data using 2D gradient‐based inversion. In comparison with the classic Cole–Cole model, it was found that for all the three re‐parameterisations, the model parameters are less correlated with each other and, consequently, better resolved for both time‐domain and frequency‐domain data. The increase in model resolution is particularly significant for models that are poorly resolved using the classic Cole–Cole parameterisation, for instance, for low values of the frequency exponent or with low signal‐to‐noise ratio. In general, this leads to a significantly deeper depth of investigation for the , , and parameters, when compared with the classic parameter, which is shown with a field example. We believe that the use of re‐parameterisations for inverting field data will contribute to narrow the gap between induced polarization theory, laboratory findings, and field applications.

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2018-07-27
2024-04-19
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References

  1. AukenE., ChristiansenA.V., JacobsenB.H., FogedN. and SørensenK.I.2005. Piecewise 1D laterally constrained inversion of resistivity data. Geophysical Prospecting53, 497–506.
    [Google Scholar]
  2. BérubéC.L., ChouteauM., ShamsipourP., EnkinR.J. and OlivoG.R.2017. Bayesian inference of spectral induced polarization parameters for laboratory complex resistivity measurements of rocks and soils. Computers & Geosciences105, 51–64.
    [Google Scholar]
  3. BinleyA., SlaterL.D., FukesM. and CassianiG.2005. Relationship between spectral induced polarization and hydraulic properties of saturated and unsaturated sandstone. Water Resources Research41, 1–13.
    [Google Scholar]
  4. BörnerF.D., SchopperJ.R. and WellerA.1996. Evaluation of transport and storage properties in the soil and groundwater zone from induced polarization measurements. Geophysical Prospecting44, 583–601.
    [Google Scholar]
  5. ChenJ., KemnaA. and HubbardS.S.2008. A comparison between Gauss‐Newton and Markov‐chain Monte Carlo‐based methods for inverting spectral induced‐polarization data for Cole–Cole parameters. Geophysics73, F247‐F259.
    [Google Scholar]
  6. ColeK.S. and ColeR.H.1941. Dispersion and absorption in dielectrics. Journal of Chemical Physics9, 341–351.
    [Google Scholar]
  7. FiandacaG., AukenE., GazotyA. and ChristiansenA.V.2012. Time‐domain induced polarization: full‐decay forward modeling and 1D laterally constrained inversion of Cole–Cole parameters. Geophysics77, E213–E225.
    [Google Scholar]
  8. FiandacaG., ChristiansenA. and AukenE.2015. Depth of investigation for multi‐parameters inversions. Near Surface Geoscience 2015–21st European meeting of environmental and engineering geophysics.
  9. FiandacaG., RammJ., BinleyA., GazotyA., ChristiansenA.V. and AukenE.2013. Resolving spectral information from time domain induced polarization data through 2‐D inversion. Geophysical Journal International192, 631–646.
    [Google Scholar]
  10. GazotyA., FiandacaG., PedersenJ., AukenE. and ChristiansenA.V.2012. Mapping of landfills using time‐domain spectral induced polarization data: the Eskelund case study. Near Surface Geophysics10, 575–586.
    [Google Scholar]
  11. HastingsW.K.1970. Monte Carlo sampling methods using Markov chains and their applications. Biometrika57, 97–109.
    [Google Scholar]
  12. HönigM. and TezkanB.2007. 1D and 2D Cole–Cole inversion of time‐domain induced‐polarization data. Geophysical Prospecting55, 117–133.
    [Google Scholar]
  13. JohanssonS., FiandacaG. and DahlinT.2015. Influence of non‐aqueous phase liquid configuration on induced polarization parameters: conceptual models applied to a time‐domain field case study. Journal of Applied Geophysics123, 295–309.
    [Google Scholar]
  14. JohanssonS., SparrenbomC., FiandacaG., LindskogA., OlssonP.‐I., DahlinT. and RosqvistH.2016. Investigations of a Cretaceous lime‐stone with spectral induced polarization and scanning electron microscopy. Geophysical Journal International, ggw432.
    [Google Scholar]
  15. KemnaA., BinleyA. and SlaterL.2004. Crosshole IP imaging for engineering and environmental applications. Geophysics69, 97–107.
    [Google Scholar]
  16. LerouxV., DahlinT. and SvenssonM.2007. Dense resistivity and induced polarization profiling for a landfill restoration project at Härlöv, Southern Sweden. Waste Management & Research25, 49–60.
    [Google Scholar]
  17. LokeM.H., ChambersJ.E. and OgilvyR.D.2006. Inversion of 2D spectral induced polarization imaging data. Geophysical Prospecting54, 287–301.
    [Google Scholar]
  18. MadsenL.M., FiandacaG., AukenE. and ChristiansenA.V.2017. Time‐domain induced polarization—An analysis of Cole–Cole parameter resolution and correlation using Markov chain Monte Carlo inversion. Geophysical Journal International.
    [Google Scholar]
  19. MalinvernoA.2002. Parsimonious Bayesian Markov chain Monte Carlo inversion in a nonlinear geophysical problem. Geophysical Journal International151, 675–688.
    [Google Scholar]
  20. Maurya, P. K., Fiandaca, G., Auken, E. and Christiansen, A. V.2016. Lithological characterization of a comtaminated site using direct current resistivity and time domain induced polarization. IP2016/4th International Workshop on Induced Polarization, 6.‐8. Aarhus, Denmark, June 2016.
  21. MetropolisN., RosenbluthA.W., RosenbluthM.N., TellerA.H. and TellerE.1953. Equation of state calculations by fast computing machines. The Journal of Chemical Physics21, 1087–1092.
    [Google Scholar]
  22. MosegaardK. and TarantolaA.2002. Probabilistic approach to inverse problems. In: International Handbook of Earthquake and Engineering Seismology (eds W.Lee , P.Jennings , C.Kisslingers , and H.Kanamori ). Academic Press.
    [Google Scholar]
  23. NordsiekS., DiamantopoulosE., HördtA. and DurnerW.2016. Relationships between soil hydraulic parameters and induced polarization spectra. Near Surface Geophysics14, 23–37.
    [Google Scholar]
  24. OlssonP.‐I., FiandacaG., LarsenJ.J., DahlinT. and AukenE.2016. Doubling the spectrum of time‐domain induced polarization by harmonic de‐noising, drift correction, spike removal, tapered gating and data uncertainty estimation. Geophysical Journal International207, 774–784.
    [Google Scholar]
  25. OlssonP.I., DahlinT., FiandacaG. and AukenE.2015. Measuring time‐domain spectral induced polarization in the on‐time:decreasing acquisition time and increasing signal‐to‐noise ratio. Journal of Applied Geophysics2015, 6.
    [Google Scholar]
  26. PeltonW.H., WardS.H., HallofP.G., SillW.R. and NelsonP.H.1978. Mineral discrimination and removal of inductive coupling with multi‐frequency IP. Geophysics43, 588–609.
    [Google Scholar]
  27. SeigelH.O.1959. Mathematical formulation and type curves for induced polarization. Geophysics24, 547–565.
    [Google Scholar]
  28. SlaterL.D. and LesmesD.2002. IP interpretation in environmental investigations. Geophysics67, 77–88.
    [Google Scholar]
  29. TarantolaA. and ValetteB.1982. Generalized nonlinear inverse problems solved using a least squares criterion. Reviews of Geophysics and Space Physics20, 219–232.
    [Google Scholar]
  30. TarasovA. and TitovK.2013. On the use of the Cole–Cole equations in spectral induced polarization. Geophysical Journal International195, 352–356.
    [Google Scholar]
  31. VanhalaH.1997. Mapping oil‐contaminated sand and till with the spectral induced polarization (IP) method. Geophysical Prospecting45, 303–326.
    [Google Scholar]
  32. WellerA., SlaterL., BinleyA., NordsiekS. and XuS.2015. Permeability prediction based on induced polarization: insights from measurements on sandstone and unconsolidated samples spanning a wide permeability range. Geophysics80, D161–D173.
    [Google Scholar]
  33. WellerA., Slater, L. & Nordsiek, S.2013. On the relationship between induced polarization and surface conductivity: Implications for petrophysical interpretation of electrical measurements. Geophysics78, D315‐D325.
    [Google Scholar]
  34. YoshiokaK. and ZhdanovM.S.2005. Three‐dimensional nonlinear regularized inversion of the induced polarization data based on the Cole–Cole model. Physics of the Earth and Planetary Interiors150, 29–43.
    [Google Scholar]
  35. Yuval and OldenburgD.W.1997. Computation of Cole–Cole parameters from IP data. Geophysics62, 436–448.
    [Google Scholar]
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