Accurate regional - residual separation by finite element approach, Bouguer gravity of a Precambrian mineral prospect in northwestern Ontario
Bibliographic record
Abstract
The regional – residual resolution of the potential field continues to be a topic of considerable interest among geophysicists even to the present time. In spite of a large number of sophisticated analytical techniques both in the space and frequency domain (Coons, et al, 1967, Oliver, 1977; Jachens and Griscom, 1985; Simpson et al, 1986; Pawlowski, 1994; Chapin, 1996), there are instances where interpreters are not satisfied with the regional and residual components obtained by these methods (for example, Gupta and Ramani, 1980) and have resorted to the intuitive graphical approach. While processing the Bouguer gravity data for a mineral prospect in a Precambrian terrain in north Western Ontario, Canada, Gupta and Ramani (1980) were not fully satisfied by the regional components obtained by spectral factorization and upward continuation. These were still found to contain a portion of the shallower effects, thereby producing residual anomalies not quite suitable for gravity modeling. Taking geology and density of the formations into consideration, Gupta and Ramani (1980) carried out graphical smoothing for residualization. On the face of the reported unsatisfactory performances of three analytical techniques, namely trend surface analysis, upward continuation and spectral factorization, often used by many interpreters, we wish to illustrate that by employing a finite element approach - FEA (Mallick and Sharma, 1999), it is possible to obtain regional and residual anomalies that compare favourably with those intuitively assumed in Precambrian terrain in northwest Ontario, Canada. To prove our point, we have reprocessed the Bouguer gravity data of Gupta and Ramani (1980) by the new approach and present the contour maps and images of the regional and residual components. The new FEA approach is based on a finite element concept described in detail with a number of examples by Mallick and Sharma (1999).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".