Spectral Analysis of Magnetometer Swing in High-Resolution UAV-borne Aeromagnetic Surveys
Bibliographic record
Abstract
Electromagnetic interference produced by multi-rotor UAVs has the potential to compromise the integrity of UAV-borne total magnetic field (TMI) measurements collected with high-resolution optically pumped magnetometers. One method to overcome this challenge involves suspending the magnetometer sensor below the zone of electromagnetic interference via a semi-rigid mount. The semi-rigid mount allows the magnetometer payload to freely move in the pitch and roll axes, while rigidly fixing the yaw of the magnetometer to that of the multi-rotor UAV. The swinging motions of the magnetometer suspended below the UAV have the potential to introduce periodic variations in the collected magnetic field data. Within this study, spectral analysis was applied to UAV-borne TMI measurements to assess contributions to the signal from the swinging, semi-rigidly mounted magnetometer payload. Overall, it was concluded that when the magnetometer was placed outside the zone of electromagnetic interference created by the UAV, compensation and filtering was not required to achieve industry standard measurements. This result was due to the magnetometer swinging through the relatively low-amplitude geomagnetic field gradient. However, when the magnetometer was placed within the zone of UAV-induced electromagnetic interference, a periodic, high-frequency signal was apparent in the TMI measurements. This was determined to be caused by the swinging of the suspended magnetometer payload within the high-gradient electromagnetic field produced by the multi-rotor UAV. The periodic signal (~0.35 Hz) was successfully identified and removed with a low-pass filter in the frequency domain, resulting in TMI measurements of industry standard quality. Filtering is a necessary step to avoid contaminating the magnetic field signals originating from sub-surface targets with unwanted signals related to the swinging of the magnetometer. Filtering can be applied when the targeted signal frequencies and the swinging signal frequencies do not spectrally overlap. This relationship must be considered in order to avoid removing important target signals during the filtering process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 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".