UAV-based magnetometry — Practical considerations, performance measures, and application to magnetic anomaly detection
Bibliographic record
Abstract
Abstract Interest in ubiquitous low-cost unmanned aerial vehicles (UAVs) for use in aeromagnetic surveying has grown dramatically over the past decade. While their appeal is alluring, caution is called for as high-quality airborne magnetometry requires diligent system design and performance qualification. This paper discusses considerations and trade-offs in UAV-based magnetometry, standard measures to qualify performance, and application to magnetic anomaly detection (MAD). The apparent simplicity of towed-bird installations needs careful consideration. Logistical complexities, stability, and safety issues aside, critical compensation for time-varying swing effects is seldom, if at all, standard practice. While well-compensated fixed-mount sensor installations are preferable, they require careful attention to a number of unique aspects including the complex magnetic signatures of typical UAVs. The paper introduces a novel anomaly detection method that is based on the entropy of the total-field magnetometer signal, gated by an analogous measure obtained from a vector magnetometer. Two field studies using a fixed-mount single-magnetometer configuration on a helicopter UAV empirically demonstrate the application of the performance measures and the performance of the MAD method. Notably, the latter clearly illustrates the importance of sound aeromagnetic compensation and enhances the output of an earlier entropy-based detection method.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".