Focusing joint inversion of gravity and magnetic data using a clustering stabilizer in a space of weighted parameters
Bibliographic record
Abstract
SUMMARY This paper develops a minimum-support focusing stabilizer to perform a joint inversion of the vertical components of gravity and magnetic data using fuzzy c-means clustering (FCM) with the regularized Newton method in a space of weighted parameters. Not only does this joint inversion technology arrive at the conditionally well-posed traditional potential field inversion, but it also increases the structural correlation between multiple inverted models. The FCM and the focusing stabilizer make it possible to balance the convergence of the data space (D) and the model space (M), guiding multimodal geophysical parameters toward assigned petrophysical values, which makes the results more stable and realistic. Two model studies are presented to illustrate the method, a simple synthetic model with two rectangular bodies in a homogenous background and a realistic model of the Volcanogenic Massive Sulfide (VMS) deposits in northeastern New Brunswick, Canada. These models demonstrate that the new focusing joint inversion algorithm produces better images than traditional methods because the FCM function uses the structural correlation of density contrast and magnetic susceptibility as constraints.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".