A Least-Squares Minimisation Approach to Depth, Index Parameter, and Amplitude Coefficient Determination from Magnetic Anomalies Due to Thin Dykes
Bibliographic record
Abstract
We have developed a least-squares approach to determine, successively, the depth, index parameter, and amplitude coefficient of a buried thin dyke, using moving-average residual anomalies obtained from magnetic data using filters of successive graticule spacings. By defining the moving-average residual anomaly value at the origin on the profile, the problem of depth determination is transformed into the problem of solving a nonlinear equation, f(z) = 0. Knowing the depth and applying the least-squares method, the index parameter is determined by solving a nonlinear equation of the form λ(θ) = 0. Finally, knowing the depth and the index parameter, the amplitude coefficient is determined in a least- squares sense using a simple linear equation. In this way, the depth, index parameter, and amplitude coefficient are determined individually from all observed magnetic data. We have developed a procedure for automated interpretation of magnetic anomalies attributable to thin dykes. We apply the method to synthetic data with random errors, complicated regionals, and interference from neighbouring magnetic rocks, and we test it on two field examples from Brazil and Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".