Effect of magnetic field direction and source orientation on depth estimation solutions
Bibliographic record
Abstract
Most semi-automated processing routines assume simple scenarios where the resultant anomaly is produced by a single, parallel-piped, non-dipping source in a vertical ambient field. This however does not represent any realworld situation. All magnetic sources will have some dip and not be oriented parallel or orthogonal to the ambient magnetic field. Therefore, the angle at which the magnetic field intercepts the source will not be the same along all edges. This discussion is further complicated when one considers whether the source is 2D or 3D, which is dependent on where calculations are conducted along the source edge. This raises the critical question of how does the magnetic field direction affect depth solutions along the source edges which each have a different orientation? Tilt-depth has been shown to work reliably under ideal conditions. Since tilt-depth is a simplified formula using the assumption of a vertical magnetic field, one needs to revisit its fundamental equations. As it turns out, the fundamental equations that give rise to tilt-depth do employ basic magnetic field geometry parameters. This shows it is incorrect to assume that the same depth solution will be produced regardless of the source orientation relative to the magnetic field. The consequence of this effect becomes apparent through a solution asymmetry on either side of the source edge. By varying the field parameters in a synthetic scenario, it is shown that variable depth solutions are achieved depending on the planar orientation of the magnetic source.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".