Application of surface-derived attributes in determining boundaries of potential-field sources
Bibliographic record
Abstract
This paper describes an edge detection method based on surface-derived attributes. The surface-derived attributes are widely used in the interpretation ofseismic datain two main categories: (1) derivative attributes including the dip angle and the azimuth; (2) derivative attributes including curvature attributes. In general, the magnitude of the normal curvature of a surface (curvature attributes) can be expressed in terms of derivatives of that surface which are called the first and second fundamental forms of the surface. For a quadratic surface which fits data, it is shown that the dip-angle equation in the interpretation of the seismic data is similar to the horizontal gradient magnitude (HGM) equation in the interpretation of potential field data. Among the infinite number of curvature attributes, a few of them which are suitable for edge detection are shown. The coefficients of a quadratic surface are calculated using the least square method. At a particular point, the attributes are obtained using these coefficients. Zero contours of most positive curvature and the determinant of the curvature matrix delineate the location of the edges of anomalies. Theshape indexattributequantitatively reflects the local shape of the surface over sources in terms of cap, dome, ridge, saddle, rut, trough and cup.Here, the maximum curvature is introduced as a new technique to detect the horizontal location of anomalies. First, the introduced attributes were applied to the noise-free synthetic data. Then, the data with the noise added were used to check the stability of the method. In the presence of high-level noise, this method was successful in determining boundaries of the anomalies.Zero contoursof the most positive curvature, the determinant of curvature matrix and the maximum curvature properly illustratethe linear features within the mapped surface. The results of using surface-derived attributes were compared with tilt angle and HGM filters. This comparison showed that zero contours of the most positive and maximum curvature attributes are approximately matched with zero contours of the tilt angle and maximum values of HGM. Finally, this method was used for real data from Mobrun massive sulfide ore of Canada.MATLAB softwarewas used for programming and calculating the required parameters of this method.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".