Robust and flexible mixed-norm inversion
Bibliographic record
Abstract
In this paper, we propose a robust mixed-norm regularization method for the inversion of geophysical data. Our regularization function is an improvement over previous methods as it can approximate any lp-norm on the range 0 ≤ p ≤ 2 applied independently to the model and its gradients. The model space can easily be divided into sub-regions with different norms to recover both smooth and compact anomalies. The inversion procedure is implemented by a modified Scaled Iteratively Re-weighted Least Squares (S-IRLS) method, improving the convergence of the algorithm. A rational is also provided for determining an adequate stabilization parameter required by the conventional IRLS method. Sparse norms are applied on a rotated objective function in order to enforce lateral continuity of oriented discrete anomalies. We first illustrate the flexibility of our formulation on a simple 1-D synthetic example. The algorithm is then implemented on an airborne magnetic data set over the Tli Kwi Cho kimberlite complex. Presentation Date: Monday, October 17, 2016 Start Time: 4:10:00 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) | 0.003 | 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 teacher head, 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".