Skip to main content

Joining multiple AEM datasets to improve accuracy, cross calibration and derived products: The Spiritwood VTEM and AeroTEM case study

Airborne time-domain electromagnetic methods (AEM) are useful for hydrogeological mapping due to their rapid and extensive spatial coverage and high correlation between measured magnetic fields, electrical conductivity, and relevant hydrogeological parameters. However, AEM data,
preprocessing and modelling procedures can suffer from inaccuracies that may dramatically affect the final interpretation. We demonstrate the importance and the benefits of advanced data processing for two AEM datasets (AeroTEM III and VTEM) collected over the Spiritwood buried valley aquifer in
southern Manitoba, Canada. Early-time data gates are identified as having significant flightdependent signal bias that reflects survey flights and flight lines. These data are removed from inversions along with late time data gates contaminated by apparently random noise. In conjunction with
supporting information, the less-extensive, but broader-band VTEM data are used to construct an electrical reference model. The reference model is subsequently used to calibrate the AeroTEM dataset via forward modelling for coincident soundings. The procedure produces calibration factors that we
apply to AeroTEM data over the entire survey domain. Inversion of the calibrated data results in improved data fits, particularly at early times, but some flight-line artefacts remain. Residual striping between adjacent flights is corrected by including a mean empirical amplitude correction factor
within the spatially constrained inversion scheme. Finally, the AeroTEM and VTEM data are combined in a joint inversion. Results confirm consistency between the two different AEM datasets and the recovered models. On the contrary, joint inversion of unprocessed or uncalibrated AEM datasets results
in erroneous resistivity models which, in turn, can result in an inappropriate hydrogeological interpretation of the study area.

In progress
Project URL
Start Date
End Date

The Great Lakes - St. Lawrence Research Inventory is an
interactive, Internet-based, searchable database created as a tool to collect and disseminate
up-to-date information about research projects in the
Great Lakes - St. Lawrence Region.