3D joint inversion of potential field data in the presence of remanent magnetization
Bibliographic record
Abstract
In this paper, we develop a method of jointly inverting airborne gravity gradiometry (AGG) and total magnetic intensity (TMI) data in the presence of remanent magnetization. The goal is to obtain structurally similar 3D density and magnetization models. In addition, in the areas with remanent magnetization, one should invert not for magnetic susceptibility, but for a 3D distribution of magnetization vector. This comes at the cost of increased non-uniqueness, which we remedy with joint inversion based on both Gramian constraints or by using joint focusing stabilizers. The Gramian structural constraints are enforced through a correlation of the model gradients. The joint focusing stabilizers are implemented using minimum support approach. We apply this novel joint inversion method to interpretation of the airborne data collected over the Thunderbird V-Ti-Fe deposit in Ontario, Canada. By combining the complementary AGG and TMI data, we generate the jointly inverted models which provide a more consistent image of the geologic structure of the area, simplifying interpretation. Presentation Date: Tuesday, October 13, 2020 Session Start Time: 1:50 PM Presentation Time: 3:05 PM Location: Poster Station 6 Presentation Type: Poster
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".