Measurement of noise in airborne gravity data using even and odd grids
Notice bibliographique
Résumé
Each of the even and odd data sets is an independent data set, separately levelled, gridded and low-pass filtered to create even and odd grids. Each data set contains a geological com- ponent and a noise component. The geological component in each data set is identical, i.e. they are both measured over the same survey area and the geological signal is well sampled on each of the odd and even data sets. The noise component is assumed to be white, containing all frequencies in equal pro- portion. Tests with AIRGrav airborne gravity data sets indi- cate that, except for the highest frequencies, which would have been filtered out of realistic gravity grids, the remaining noise is very close to white. Gravity has been measured from aircraft in flight since the late 1950s (Thompson & LaCoste 1960). Recent improvements in GPS processing, and a new gravity instrument, the AIRGrav system (Argyle et al. 2000), have resulted in significantly re- duced noise levels in airborne gravity data. In this paper we present a methodology to quantitatively calculate noise levels of airborne gravity data sets by dividing the flight lines into two equal data sets (the 'even' and 'odd' lines), gridding and filtering the separate data sets, and measuring the difference between the resultant grids. The data is low-pass filtered be- fore the noise level is measured, and noise levels are calculated for specific filter lengths. We also present an example of this noise calculation performed on an AIRGrav data set from the foothills region of Alberta, Canada, along with an interpreta- tion of the data (Peirce et al. 2002; Sander et al. submitted). AIRGrav airborne gravity data is generally acquired along survey lines spaced between 50 and 3000 m, flown in a grid pattern over the survey area. After normal gravity corrections, data is gridded and filtered to remove high frequency GPS and gravity acquisition noise. On many AIRGrav surveys, SGL over-samples the gravity field to increase the accuracy and resolution of the resultant data. Noise from the over-sampled gravity data cancels in a manner similar to the stacking of seis- mic data. The over-sampled gravity data can be used to calcu- late the noise level on a gravity grid by dividing the data set into two independent data sets covering the same area, and calculating the RMS difference between them. As the data sets cover the same area, the geological signal will cancel, leaving only the noise of the two data sets. The RMS noise measured on the difference grids will be twice the noise level of the com- bined grid, as explained below. In this case 'noise' means the errors between lines or indi- vidual readings within the data set. The method would not measure systematic errors common to the entire data set. Sys- tematic errors could occur if the entire data set was levelled to some predetermined value, or if the same erroneous elevation model was used for terrain corrections for both the odd and even data sets.
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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 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».