Edge enhancement of potential field data using an enhanced tilt angle
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Bibliographic record
Abstract
AbstractWe 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.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, is presented. The new filter clearly enhances the edges of superimposed sources sharply and generates more subtle detail. Key words:: edge enhancementpotential fieldssuperimposed sourcestilt angle AcknowledgementsThe authors acknowledge the support of the National Science and Technology Specific Project (2011ZX05005–005–009HZ, 2011ZX05023–003–003), and Specialised Research Fund for the Doctoral Program of Higher Education (20110072110017). They also thank the Geological Survey of Canada (GSC) for permission to use the gravity and magnetic data in Figures 5a and 6a, and the anonymous reviewers for their constructive comments on the manuscript.
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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 | 0.001 |
| 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 it