Edge enhancement of potential field data using an enhanced tilt angle
Bibliographic record
Abstract
We present an edge-detection technique for the enhancement of potential field data, which is based on the tilt angle of the first order vertical derivative of the total horizontal gradient. The technique can be performed using three steps, as follows: first, we calculate the total horizontal gradient of the potential fields, which is stable and effective in determining the horizontal locations; second, we calculate the first order vertical derivative of the total horizontal gradient to increase the vertical-resolution on the basis of the determined the horizontal locations; finally, we display the tilt angle of the first order vertical derivative of the total horizontal gradient tending to balance the amplitude responses from both shallow and deep sources. This technique is designed to reflect the complex distributions of multiple sources with different depths and extents. The effectiveness of our method is demonstrated by synthetic data. The results indicate that the new filter generates more subtle detail for superimposed sources, compared with other edge detection filters. The method is also applied to field surveyed data from the Saskatoon area of Canada, and the results are helpful for qualitative interpretation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".