Drape corrections of aeromagnetic data using wavelets
Bibliographic record
Abstract
Aeromagnetic surveys are commonly flown at a constant height above the terrain to minimise the magnetic effects of variable terrain clearance. This is known as drape flying. However, in mountainous regions it is often not operationally feasible to perform a drape survey. Instead, the survey is flown at a constant barometric height and the draped magnetic data are calculated numerically using a level-to-drape continuation operator. Existing techniques for this calculation include the chessboard and Taylorseries methods. An alternative method described here, based on the wavelet transform, approaches the problem by representing the continuation integral using a family of wavelet basis-functions localised in both space and frequency. This allows the generation of a set of coefficients that can be efficiently applied to the wavelet transform of the signal. The wavelet approach can be used for both 1D and 2D signals. If the drape surface is closer to the ground than the barometric survey height, a major difficulty in the drape correction is the control of noise. This is achieved in the wavelet domain by using a locally-adaptive, exponential noise-reduction filter which can be designed based on the wavelet coefficients. The method can be extended in some cases to generate draped images below the ground surface that can be used to sharpen images of magnetic basement in sedimentary basins. The wavelet method is compared with conventional techniques using data from the Edge Hills region in Canada and the Browse Basin in Western Australia. In this study, the wavelet approach combined with the exponential smoothing filter produces sharper images than either the chessboard or Taylor-series methods.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".