Frequency-domain elastic FWI for VTI media
Bibliographic record
Abstract
In this study, a frequency-domain elastic full waveform inversion algorithm for 2-D VTI media has been developed. The forward problem used in this inversion algorithm is simulated by applying frequency domain finite difference method, which is a fast approach for multisource and multi-receiver acquisition. For the anisotropic inversion of VTI media, five elastic constants (c11, c13, c33, c44 and density) have to be dealt with. In this paper, the gradients of four elastic constants are calculated in matrix forms. To accelerate the convergence rate of inversion, the pusdo-Hessian is also implemented in the objective function. The inversion results in the paper indicate that parameters c11, c33 and c44 can be inverted properly, yet c13 can not be inverted properly due to the severe crosstalk. Presentation Date: Wednesday, October 17, 2018 Start Time: 1:50:00 PM Location: 207C (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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".