Joint inversion of airborne electromagnetic and total magnetic intensity data using Gramian structural constraints: Case study of the Reid-Mahaffy test site in Ontario, Canada
Bibliographic record
Abstract
Acquisition of airborne electromagnetic (AEM) data is usually combined with the total magnetic intensity (TMI) surveying, making these two geophysical methods a natural choice for joint inversion. In this paper, we present an algorithm for joint inversion of the frequency or time domain airborne electromagnetic (AEM) and TMI data producing structurally similar 3D conductivity and susceptibility models. The method is based on the structural Gramian constraints (Zhdanov et al., 2012; Zhdanov, 2015), which enforce structural correlations of the gradients of different physical property models. The method is illustrated by the results of inverting the frequency-domain DIGHEM AEM and airborne magnetic data collected over the Reid-Mahaffy test site in Ontario, Canada. By combining these complementary datasets, we produce subsurface images of geological structures with the sharper boundaries, stronger structural correlations, and with the same level of data misfit as the standalone inversions. Presentation Date: Wednesday, October 14, 2020 Session Start Time: 9:20 AM Presentation Time: 9:20 AM Location: Poster Station 7 Presentation Type: Poster
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".