On the roles of magnetization and topography in the scaling behaviour of magnetic-anomaly fields
Bibliographic record
Abstract
There is lack of agreement on the underlying cause of widely observed power-law scaling behaviour of magnetic-anomaly fields. Some workers ascribe this behaviour to intrinsic 3-D fractal distributions of magnetization in the crust of the Earth; others point to a power-law exponent β∼ 3, expected for a random ensemble of statistically independent magnetized prisms: the classic Spector & Grant model (SG model). We apply a perturbation approach to the Parker model to derive expressions for the power spectra of magnetic-anomaly fields in the presence of laterally varying magnetization and/or topography at the top of magnetic basement. Under appropriate assumptions, our modified Parker model reduces to either an SG model or a 2-D fractal model. In the case of fractal magnetization without topography, the power-law slope of the magnetic-anomaly field (after depth correction) is equal to the power-law slope for the magnetization distribution. In the case of fractal basement topography alone, the power-law slope is reduced by 2. Where both the magnetization and topography are fractal, the effects of magnetization tend to dominate the power-law behaviour of the associated magnetic-anomaly field. Two real-data examples from the Canadian Shield exhibit power-law exponents of 2.02 ± 0.02 and 1.42 ± 0.01, within a wavelength band of 2 to 100 km. These slopes are significantly different from previously cited values of ∼3, casting doubt on the general applicability of the β∼ 3 slope that is inherent to SG models at high wavenumber.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".