Joining multiple AEM datasets to improve accuracy, cross calibration and derived products: The Spiritwood VTEM and AeroTEM case study
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT 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 flight‐dependent 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it