Cooperative magnetic inversion
Bibliographic record
Abstract
The effect of remanence has long been recognized as an obstacle for the interpretation and modeling of magnetic data. In this paper, we propose a Cooperative Magnetic Inversion (CMI) algorithm for the 3-D inversion of magnetic data affected by remanent magnetization. The CMI algorithm incorporates advantages from two inversion strategies. Magnetic amplitude data are first inverted to recover an effective susceptibility model, providing information about the geometry and extent of the magnetic anomaly. The effective susceptibility model is then used to constrain a Magnetic Vector Inversion (MVI), recovering the orientation and magnitude of magnetization. We test the CMI algorithm on a ground magnetic survey over the Osborne Cu-Au deposit, Queensland. In both the case study and the synthetic experiments, the cooperative approach improves the resolution of magnetized bodies over each of the inversion methods used separately. Presentation Date: Monday, October 17, 2016 Start Time: 3:20:00 PM Location: 161 Presentation Type: ORAL
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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; both teacher heads agree on what is shown here.
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".