Normalized vertical derivatives in the edge enhancement of maximum-edge-recognition methods in potential fields
Bibliographic record
Abstract
ABSTRACT Gravity and magnetic data have unique advantages for studying the lateral extents of geologic bodies. There is a class of methods for edge recognition called maximum-edge-recognition methods (MERMs) that use their extreme values to locate the edges of geologic bodies. These methods include the total horizontal derivative (THDR), the analytic signal amplitude, the theta map, and the normalized standard deviation. These are all first-order derivative-based techniques. There are also higher-order derivative-based methods that are derived from the first-order filters, for example, the THDR of the tilt angle. We have developed an edge-recognition filter that is based on the idea of the normalized vertical derivatives (VDRs) of existing methods. For each MERM, we first calculate its nth-order VDR and then use thresholding to locate its peaks. The peak values are subsequently normalized by the values of the original MERM. Testing on synthetic and real data indicates that the normalized VDRs of the MERMs have higher accuracy and better lateral resolution and they are more interpretable than existing techniques; thus, they are a worthwhile addition to the set of edge-detection tools for potential-field data.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".