Comparison of geophysical inversion programs run on aeromagnetic data collected over the Highland Valley Copper district, British Columbia, Canada
Bibliographic record
Abstract
As part of the Natural Sciences and Engineering Research Council of Canada–Canada Mining Innovation Council (NSERC-CMIC) Mineral Exploration Footprints project, three selected magnetic inversion programs (VPmg, MAG3D and VINV) were used to process the same aeromagnetic data set from the Highland Valley Copper district, British Columbia, Canada. In each case, the inversion was constrained using available geological and physical property constraints. Analysis of magnetic susceptibility data suggests that the observed aeromagnetic anomaly pattern includes effects associated with boundaries between lithological units and fault zone alteration resulting from removal of magnetite. Susceptibility contrast associated with alteration is greater than that associated with changes in lithology. The inversions seek to define the three-dimensional geometry of geological boundaries and the fractures are treated as high-frequency noise. Results from the three programs, although similar, are sensitive to attributes of the different algorithms. VPmg emphasises physical boundaries between geological domains, MAG3D produces a more blurred image, whereas VINV produces reasonable geological images. Computer performance using the different programs ranges from reasonable for VPmg to computer intensive for MAG3D and VINV. Differences in the results reflect the inherent uncertainty in producing inversions from “noisy” aeromagnetic data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".