Research Note: A proposed procedure for ameliorating edge effects in magnetic data transformations
Bibliographic record
Abstract
ABSTRACT Reduction to pole and other transformations of total field magnetic intensity data are often challenging to perform at low magnetic latitudes, when remanent magnetization exists, and when large topographic relief exists. Several studies have suggested the use of inversion‐based equivalent source methods for performing such transformations under those complicating factors. However, there has been little assessment of the importance of erroneous edge effects that occur when fundamental assumptions underlying the transformation procedures are broken. In this work we propose a transformation procedure that utilizes magnetization vector inversion, inversion‐based regional field separation and equivalent source inversion on unstructured meshes. We investigated whether edge effects in transformations could be reduced by performing a regional separation procedure prior to equivalent source inversion. We applied our proposed procedure to the transformation of total field magnetic intensity to all three Cartesian magnetic field components using a complicated synthetic example based on a real geological scenario from mineral exploration. While the procedure performed acceptably on this test example, the results could be improved. We pose many questions regarding the various choices and control parameters used throughout the procedure, but we leave the investigation of those questions to future work.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".