Joint multinary inversion of gravity and magnetic data using Gramian constraints
Bibliographic record
Abstract
This paper introduces a novel approach to the joint inversion of gravity and magnetic data based on multinary transformation of the model parameters and Gramian constraints. By combining these two concepts, the joint multinary inversion using Gramian constraints not only makes it possible to explicitly exploit the sharp contrasts of the density and magnetic susceptibility between the host media and anomalous targets in the inversion of gravity and magnetic data, but also provides consistent spatial boundaries of the anomalous targets in the distributions of density and magnetic susceptibility. We demonstrate that, this method can be effectively used for the joint inversion of the full tensor gravity gradiometry (FTG) and the total magnetic intensity (TMI) data by applying the developed algorithm to the field data collected in the area of the McFualds Lake in northwestern Ontario, Canada. The joint inversion results provide a geological model with high resolution for the exploration of magmatic chromite deposits. Presentation Date: Tuesday, October 16, 2018 Start Time: 8:30:00 AM Location: 213B (Anaheim Convention Center) Presentation Type: Oral
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".