Contact mapping from gridded magnetic data - a comparison of techniques
Bibliographic record
Abstract
Delineating the edges of magnetised bodies is a fundamental application of magnetic data to geological mapping in areas of limited exposure. Especially in Precambrian shield-like regions, locating lateral changes in magnetisation of the outcropping crystalline rocks provides spatial information that is crucial in extending mapped geology into sparsely exposed or completely covered areas. Although not all magnetic contacts correspond to lithological contacts, the former provide key information on structural regimes, deformation styles and trends, and magnetic texture.Many techniques for contact mapping have been developed, some originally based on profile (2-D) data and others designed specifically for grid-based (3-D) data sets. Here, we evaluate five methods applied to gridded data. The first three are based on finding maxima of the horizontal gradient magnitude of the total field (TF-hgm), tilt (TI-hgm) and pseudogravity (PSG-hgm). The fourth and fifth methods rely on locating maxima of the analytic signal (AS) and the 3-D local wavenumber (LW).Method TF-hgm produces theoretically correct contact locations only when the data are reduced to the pole, and even then may produce false or secondary solutions mimicking contact trends. Method TI-hgm is less sensitive to field direction but also suffers from secondary maxima. Method PSG-hgm is perhaps the most established approach of those mentioned, and in the case of vertical contacts produces reliable maxima. However, knowledge of remanent magnetisation direction is required. Methods AS and LW theoretically produce maxima directly over contacts and are insensitive to magnetisation direction but are more sensitive to noise than the former, which limits their application to higher quality datasets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".