1887

Abstract

Summary

The planning, contracting, data acquisition and processing plus the inverter’s quality assessment and inversion of a regional airborne electromagnetic (AEM) survey may take a few months while the interpretation is considerably more complex and comprehensive process. Most often an interpretation necessitates additional data that are more time consuming to collect and considerably more complicated to integrate into an overall model, e.g. borehole logs, borehole core samples, water chemistry, surface vegetation, satellite imagery plus the generally accepted geological background knowledge.

Interpretation basically has to do with identifying categories and finding boundaries between them so that depths, thicknesses, and a whole range of other model attributes can be quantitatively estimated. In this abstracts I will present two methods of finding attributes intended to assist the interpreter using the Continuous Wavelet Transform: One finds layer boundaries in the smooth multi-layer models that are most often used in the inversion of large data sets; and the other finds the natural categories of the model parameter. Naturally, being based on the subsurface conductivity distribution, the boundaries and categories suggested are useful only to the extent that they coincide with geological/hydrogeological boundaries and categories - which is for the interpreter to decide.

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/content/papers/10.3997/2214-4609.201702168
2017-09-03
2020-07-11
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References

  1. ChristensenN.B.
    2016a. Fast approximate 1D modelling and inversion of transient electromagnetic data. Geophysical Prospecting, 64, 1620–1631. doi: 10.1111/1365‑2478.12373
    https://doi.org/10.1111/1365-2478.12373 [Google Scholar]
  2. 2016b. Strictly horizontal lateral parameter correlation for 1D inverse modelling of large data sets. Near Surface Geophysics, 14, 391–399. doi: 10.3997/1873‑0604.2016028.
    https://doi.org/10.3997/1873-0604.2016028 [Google Scholar]
  3. CooperG.R.J. and CowanD.
    2009. Blocking geophysical borehole log data using the continuous wavelet transform. Exploration Geophysics40(2), 233–236. URL http://dx.doi.org/10.1071/EG08127.
    [Google Scholar]
  4. DavisA.C. and ChristensenN.B.
    2013. Derivative analysis for layer selection of geophysical borehole logs. Computers & Geosciences, Volume 60, October 2013, Pages 34–40. http://dx.doi.org/10.1016/j.cageo.2013.06.015
    [Google Scholar]
  5. LawrieK.C. and ChristensenN.B.
    2015. Novel AEM acquisition strategies for groundwater resource and managed aquifer recharge mapping in the Australian landscape context. Near Surface Geoscience, Turin, Italy, 6–10 September 2015.
    [Google Scholar]
  6. Mallat, S.
    , 1998. A Wavelet Tour of Signal Processing: Academic Press.
    [Google Scholar]
  7. Sørensen, K. I., and LarsenF.
    , 1999. Ellog Auger Drilling: 3-in-one Method for Hydrogeological Data Collection, Ground Water Monitoring & Remediation, 19, 4, 97–101.
    [Google Scholar]
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