Self-organizing maps for pseudo-lithological classification of 3D airborne electromagnetic, gravity gradiometry and magnetic inversions
Bibliographic record
Abstract
SummaryTo improve mineral exploration success, there is an accepted need to increase the “discovery space” by exploring under cover and to greater depths using 3D geological modelling supported by multiple 3D geophysical inversions. To facilitate this approach, multi-sensor airborne platforms capable of simultaneously measuring electromagnetic, gravity, and magnetic data are now being deployed. The availability of data from such systems poses a significant challenge to the exploration geophysicist: How do you generate a shared earth model that satisfies all data? We address this with a case study from the Reid-Mahaffy test site in Ontario, demonstrating how multiple 3D inversions of MEGATEM time-domain electromagnetic, FALCON gravity gradiometry and TMI data can be analysed by the self-organizing maps (SOM) data mining approach to produce 3D pseudo-lithological models. The results of our analyses are shown to be in agreement with the known geology of the Reid-Mahaffy 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".