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.