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TitleCapacitively coupled resistivity inversion using effective dipole lengths
LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorOldenborger, G AORCID logo; LeBlanc, A -MORCID logo
SourceGeological Survey of Canada, Technical Note no. 6, 2013, 10 pages, Open Access logo Open Access
PublisherNatural Resources Canada
Mediaon-line; digital
File formatpdf
Subjectsgeophysics; Nature and Environment; resistivity; resistivity surveys; permafrost; models; modelling
Illustrationslocation maps; models; histograms
ProgramClimate Change Geoscience
Released2014 01 08
AbstractNoncontacting capacitively coupled resistivity (CCR) surveys find application in permafrost investigations and investigations over engineered surfaces. We have observed discrepancies between line-antenna CCR data and galvanic-resistivity (GR) data. Corresponding inverse models exhibit differences in both resistivity magnitude and structure. We have applied and tested the concept of effective dipole length for line-antenna CCR data collected over permafrost terrain in Iqaluit, Nunavut. We have compared inversions of corrected CCR data to the GR counterpart. Correcting CCR data with an effective dipole length of 80% of the physical antenna length results in a resistivity model most in accordance with the GR model. However, even after correction, the CCR model does not precisely emulate the GR model.
Summary(Plain Language Summary, not published)
Non-contacting capacitively-coupled resistivity (CCR) surveys find application in permafrost investigations and investigations over engineered surfaces such as at Iqaluit International Airport, Nunavut. CCR geophysical surveys are useful for mapping geological and permafrost terrain units, and for estimating their subsidence potential associated with permafrost degradation. Subsidence can have significant detrimental impacts on land-based infrastructure. We examine the accuracy of CCR data and test a hypothesis for improving the reliability of models generated from these data. Simple manipulation of the CCR data results in models that are more consistent with other data sets. Results allow for better estimation of material properties (subsidence potential) and better comparison of data over a regional area.

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