Enhancing magnetic source edges using the tilt angle of the analytic‐signal amplitudes of the horizontal gradient
Bibliographic record
Abstract
Abstract Enhancing magnetic data is often complicated due to the non‐vertical orientation of the geomagnetic field and the orientation of remanent source magnetization. The complication can be reduced by reducing the data to the pole (mathematically making the geomagnetic field vertical), but this reduction process is problematic. The analytic‐signal amplitude can be used to enhance the edges of two‐dimensional sources without a reduction to the pole. However, the shape of the analytic‐signal amplitude is weakly dependent on the magnetization direction for grid data. This study presents an improved technique, namely the tilt angle of the analytic‐signal amplitudes of the horizontal gradient of the vertical integral. This quantity is also only weakly dependent on the magnetization direction and outlines the edges as well or somewhat better than other methods. It also implicitly involves second derivatives of the magnetic field, and we use synthetic data to demonstrate that noise is not amplified as much as it is when using other edge enhancement techniques that implicitly use second derivatives. A dataset of the Apiaí Terrane, Brazil, shows good lateral continuity of features compared with other edge‐enhancement methods, and subtle features like faults are easier to identify in the images generated by our new method. Upward continuation of the field, which is normally required, was not necessary to reduce the impact of noise on this field example.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".