On Compensating for Magnetometer Swing in UAV Magnetic Surveys
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».