Practical considerations in the use of edge detectors for geologic mapping using magnetic data
Bibliographic record
Abstract
ABSTRACT Locating the edges of magnetized sources provides a fundamental tool in the geologic interpretation of magnetic field data. Much recent effort has been expended on developing improvements to existing edge-detection methods, resulting in purported increases in accuracy and continuity along edges, reduction of noise effects, and limiting the influences of variable depth to source, magnetization direction, and source dip. These endeavors are valuable and provide interpreters with a wider range of tools to carry out geologic interpretations of aeromagnetic data. Nevertheless, survey parameters such as flight height and line spacing impose limits on the quality of edge locations that can be achieved. Using model studies, we quantify the effects that source size, depth, and interference between sources have on calculated edge locations. Based on the known behavior of established edge detectors, we found that many of the newer approaches offer limited advantages over older methods. Consequently, we studied an example of field mapping of geologic contacts in the Canadian Shield, supported by aeromagnetic data, using calculation of a standard edge detector: the horizontal gradient magnitude of the total magnetic field or TF-hgm. Calculated edge locations estimated from this method appear sufficiently accurate and continuous to provide a solid basis on which the mapping campaign was based and executed successfully.
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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 | 0.042 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".