On Compensating for Magnetometer Swing in UAV Magnetic Surveys
Bibliographic record
Abstract
Natural resource exploration has advanced in recent years through integrating unmanned aerial vehicles (UAVs) with high-resolution magnetometer payloads. One design consideration when integrating these systems for mineral exploration applications is ensuring that the magnetic measurement quality is comparable to the previously established methods of terrestrial magnetic and aeromagnetic surveying. High-resolution optically pumped magnetometers, employing a resolution of 0.1 - 0.01 nT, are the standard magnetic sensors used in both manned terrestrial magnetic and aeromagnetic surveys. Integrating a high-resolution optically pumped magnetometer in a multi-rotor UAV payload bay will compromise the integrity of the total magnetic intensity (TMI) measurements due to the electromagnetic interference generated by the brushless permanent magnet synchronous motors and other onboard electromagnetic components. One solution involves physically suspending the high-resolution magnetometer below the resolvability limit of the electromagnetic interference via a semi-rigid mount. However, the swinging motions of the high-resolution magnetometer through the geomagnetic field while in this configuration have the potential to introduce periodic variations in the collected TMI data, compromising quality. Within this study, a UAV-borne aeromagnetic survey was conducted over a mineral exploration target to assess the potential impact of magnetometer swing on collected UAV-borne TMI data. A DJI-S900 multi-rotor UAV and a GEM Systems Potassium Vapour Magnetometer (GSMP-35U) were used to fly a 500 m by 700 m grid, using a line spacing of 25 m and a flight elevation of 35 m above the ground.The optically pumped magnetometer was suspended outside the resolvability limit of the electromagnetic interference below the UAV via a semi-rigid mount. A nine degrees of freedom inertial measurement unit (IMU) was fixed to the semi-rigid mount and a Kalman filter was applied to post-process the measurements calculating the positional variations (pitch, yaw and roll) of the magnetometer. Spectral analysis was applied to the UAV-borne TMI measurements and the IMU positional data assessing contributions to the TMI signal from the swinging, semi-rigidly mounted magnetometer. Periodic signals were observed within the recorded TMI data directly relating to the swinging frequency of the magnetometer in pitch and roll throughout flight. The amplitude of the periodic TMI variations was variable (< 1 nT – 5 nT) throughout the survey and depended on the horizontal gradient of the ambient magnetic field and the arc length of the magnetometer swing. The magnetometer swinging frequency (~0.35 Hz) was determined to be primarily dependant on the magnetometer suspension length. Overall, the wavelength of the periodic TMI variations due to the swinging motions was characterized with the IMU measurements and determined to be spectrally unique from the longer wavelength geological signals targeted within the survey area. Due to the wavelengths of the targeted and untargeted signals not spectrally overlapping, the TMI variations related to magnetometer swing noise were filtered out. The design factors controlling the wavelengths of the targeted geologic signals (flight speed) and untargeted magnetometer swing noise (suspension length) must be considered when integrating high-resolution magnetometers on multi-rotor UAVs, such that the wavelengths do not spectrally overlap and phase-based compensation algorithms are not required.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".