Noise reduction procedures for gravity-gradiometer data
Bibliographic record
Abstract
ABSTRACT Noise suppression of airborne gravity-gradiometer data is a crucial part of the data processing stream. We considered two approaches to removing noise: kriging and directional filtering. Kriging is an estimation procedure for the interpolation of spatial data. The estimator is calculated from the data variogram, which characterizes the noise level and correlation length of the measurements. Directional filtering uses a user-defined operator that is oriented to preferentially smooth the data along the strike, but it leaves short-wavelength components in the cross-strike direction for definition of the trend edges. Both methods were applied to a recently collected offshore gravity gradient survey. The kriging and directional filtering results revealed a similar level of smoothness, but the main difference between them was the extra smoothing along the strike for the directionally filtered data. Because kriging is a data-driven procedure, it provides an objective estimate of the data noise level and degree of smoothness. The processing parameters required for directional filtering can then be chosen to give a similar level of smoothness and noise suppression to the kriging results, but with the added advantage of directional smoothing, which more effectively delineates geologic trends in the data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".