Analytic Signal and Euler Depth Interpretation of Magnetic Anomalies: Applicability to the Beatrice Greenstone Belt
Bibliographic record
Abstract
We apply the Analytic Signal and Euler depth filtering techniques on magnetic data to identify a magnetic causative body location-depth relationship, two parameters of importance in both geophysical exploration and ore body modelling. We identify a dipping magnetic contact from the interpreted Euler depth anomalies, showing a good agreement with both the Total Field Magnetic (TFM) map and the Analytic Signal (AS) map. The Euler depth anomalies correlate well with the locations and edges of shallow causative bodies. The deeper Euler interpreted sources explain the magnetic high on the regional aeromagnetic map which is coincident with neither geological contacts nor the more recent dolerite intrusions. This suggests that the magnetic highs on the regional aeromagnetic map are due to deep seated sources, otherwise invisible on the regional geological map. The results show the usefulness and relevancy of these two filters not only in interpreting routine TFM data from the study area, but up to a regional scale. While the aeromagnetic data shows that the magnetisation pattern is predominantly divorced from the geological map, the ground magnetic data interpretation points to a more recent magnetisation of the belt, enabling conclusions to be drawn about the geological history and structural geology otherwise not evident on the geological map.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".