Combining airborne electromagnetic data from alternating flight directions to form a virtual symmetric array
Bibliographic record
Abstract
Abstract Fixed-wing towed-bird airborne electromagnetic (AEM) systems are asymmetric because the receiver flies behind and below the transmitter. As a consequence, the measured response is quite different when the aircraft flies a traverse line in the reverse direction, even when the causative bodies are symmetric. Because fixed-wing AEM survey traverses are parallel and are flown in alternating directions, the response of bodies can change markedly from one line to the next. This means that images of the measured data are complicated and difficult to interpret. In a survey in Quebec, Canada, each traverse line was flown twice, once in the normal direction and once in the reverse directions. These data were combined to give a response measured by a symmetric system termed the virtual symmetric array (VSA). The VSA response can enhance the S/N ratio, and the response will be symmetric if the con ductive targets are symmetric. Hence, any response asymmetry is indicative of asymmetry in the ground. This means that dip direction can be inferred from the VSA response. Images of VSA data show similar properties, making them a very useful tool for interpreting fixed-wing EM data. A field example is used to illustrate that the standard presentations (filtered images and energy envelope images) are smeared and blocky, whereas the VSA images show sharper resolution, better trending, and better subtle structural features on maps. In most cases, data are not collected in reverse-line directions, but it is possible to create an interpolated VSA image using the reverse line direction data from adjacent lines. When this process is applied to field data, the resulting images have all of the advantage of VSA images, except for somewhat lower S/N ratio improvements. Also, short strike-length features are elongated, and sudden changes in amplitude are not well imaged.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".