3D inversion of QMAGT airborne magnetic gradiometry data for susceptibility and magnetization vector models of the Thompson Nickel Belt in Manitoba, Canada
Bibliographic record
Abstract
Following recent advances in SQUID technology, full tensor magnetic gradiometry (FTMG) is emerging as a practical exploration method. We introduce 3D regularized focusing inversion based on the Gramian regularization and moving sensitivity domain to interpret the FTMG data efficiently. A helicopter-borne QMAGT full tensor magnetic gradiometry (FTMG) survey was conducted over the Thompson Nickel Belt by Dias Airborne contracted by Vale Canada LTD. This project aimed to study the location and structure of the P2 member sulfidic meta sedimentary rocks of the Paleoproterozoic Ospwagan Group hosting the Mystery ultramafic intrusion and the associated Ni sulfide mineralization proximal to the survey area. The observed FTMG and calculated total magnetic intensity (TMI) data were analyzed and inverted separately using the developed 3D inversion methods. The data were inverted for susceptibility and magnetization vector models. This is one of the first papers to present the applied inversion of FTMG data towards a magnetization vector model. We also present a comparison of the inversions using the FTMG data and the calculated TMI data.
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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 | 0.000 | 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".