A sigmoid stabilizing function for fast sparse 3D inversion of magnetic data
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Bibliographic record
Abstract
ABSTRACT An interesting geological objective of quantitative interpretation of magnetic data is to find inverse models which can determine sharp geological interfaces below the surface. The stabilizing function in the Tikhonov parametric functional governs sparseness constraint in the recovered model. This paper introduces a novel stabilizer based on a sigmoid function which can provide non‐smooth models in the inversion of magnetic data efficiently. An inversion algorithm is developed based on the reweighted regularized conjugate gradient to get the solution of the inverse problem using this stabilizing function. The performance of the proposed algorithm is checked on two synthetic data sets and real aeromagnetic data from McFaulds Lake in Ontario, Canada, in comparison with the results of the minimum support stabilizing function. The inverse problem converges to the solution faster when the sigmoid stabilizing function is used instead of the minimum support stabilizing function.
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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) | 0.001 | 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 it