Curvature analysis to differentiate magnetic sources for geologic mapping
Bibliographic record
Abstract
ABSTRACT Curvature of a surface is typically applied in seismic data interpretation; however this work outlines its application to a potential field, specifically aeromagnetic data. The curvature of a magnetic grid (from point data) is calculated by fitting a quadratic surface within a moving window at each grid node. The overall and directional curvatures calculated within this window provide insight into the geometry of the magnetic grid surface and causative sources. Curvature analysis is an in‐depth study of both qualitative (graphically) and quantitative (statistically) approaches. This analysis involved the calculation of full, profile and plan curvatures. The magnitude, sign and relative ratios enable the user to define source location and geometry and also discriminate source type; for example, differentiation between a fault and normal polarity dyke. The reliability of the analysis is refined when a priori geological knowledge is available and basic statistics are considered. By allotting a weighting scheme to various statistical populations (e.g., standard deviation), increased detail is extracted on the different lithologies and structures represented by the data set. Furthermore, the curvature's behaviour is analogous to derivative calculation (vertical, horizontal and tilt) by producing a zero value at the source edge and either a local maxima or minima over the source. Application prior to semi‐automated methods may help identify correct indices necessary for identification of magnetic sources. Curvature analysis is successfully applied to an aeromagnetic data set over the 2.6–1.85 Ga Paleoproterozoic Wopmay orogen, Northwest Territories, Canada. This area has undergone regional and local‐scale faulting and is host to multiple generations of dyke swarms. As the area has been extensively mapped, this data set proved to be an ideal test site.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".