Transformation of magnetic data to the pole and vertical dip and a related apparent susceptibility transform: Exact and approximate approaches
Bibliographic record
Abstract
ABSTRACT The dipolar character of magnetic data means that there is a high and a low associated with each source. The relative positions and sizes of these highs and lows vary depending on the magnetic latitude or the inclination of the earth’s magnetic field. One method for dealing with this complexity is to transform the data to what would be collected if the inclination were vertical (as at the magnetic pole), a process that is unstable at low magnetic latitudes. Unfortunately, remanent magnetization adversely impacts the success of this transformation. A second approach is to calculate the analytic-signal amplitude (ASA) of the data, which creates a single positive feature for each source or edge, with the shape being only weakly dependent on the inclination and the presence of remanent magnetization. The ASA anomalies can appear to be relatively broad, so features sometimes merge together on map views of the ASA. A subsequent transformation of the ASA using an appropriate transforming tilt angle can generate a magnetic field of a body that is at the pole and has a vertical dip. The transformation is exact for contacts when calculated from the first-order ASA, but the sign of the transformed data can be incorrect depending on whether you are over one edge or the other edge of a discrete source body. Another approximate transformation of the zeroth-order ASA does not have this issue and gives good results on synthetic data provided that any noise is handled appropriately. The resulting maps outline the magnetic source bodies and have amplitudes proportional to an apparent magnetic susceptibility. On field data from Black Hill, South Australia, the approximate transformation generates an image that is simple to interpret and enhances some features less obvious on other enhancements of the 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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".