Focusing AVO inversion based on the minimum gradient support regularization
Bibliographic record
Abstract
Generally, regularization methods are adopted to decrease the non-uniqueness and instability of AVO inversion. In order to solve fuzzy boundaries and low focusing, the minimum gradient support (MGS), as one of regularization methods, is introduced to carry out pre-stack three-term AVO inversion for the first time in the paper. Then, considering the different orders of magnitude of P-impedance, S-impedance and density, we extend the traditional univariate MGS into trivariate MGS. In addition, in order to render the inversion more stable, a low-frequency constraint is also introduced to the objective function. Then, 1-D and 2-D models are designed to test the adaptability and reliability of the method. Numerical model applications show that the inversion method based on MGS is superior to the traditional model-based AVO inversion in preserving sharp boundaries. Furthermore, inverted results from MGS AVO inversion have a higher resolution than those from the traditional method. In the meantime, although the synthetic data is contaminated by noise, reasonable and reliable results can still be obtained from MGS inversion. All advantages guarantee that the focusing MGS AVO inversion will have a great potential for real data application in the near future. Presentation Date: Thursday, September 28, 2017 Start Time: 8:30 AM Location: 370D 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".