Bibliographic record
Abstract
Summary The full waveform inversion in heterogeneous media is a highly non-linear optimization problem. Using traditional methods like Gauss Newton, the solution is highly sensitive to the initialization point. The best results are often achieved by a frequency continuation strategy where the process is initially obtained with the low-frequency data only, and then gradually the high frequency data is added into the optimization. However, low frequency data is usually missing, and in its absence the FWI process reaches a local minimum. Travel time tomography, unlike FWI, is less sensitive to its initialization and contains low frequency features. In this work we propose a framework to jointly apply FWI and travel time tomography, so that the resulting model is faithful to both the waveform and travel time data. Using this approach we also aim to relieve the problem of missing low-frequency data. To this end, we minimize a sum of the FWI and travel time tomography misfit functions, with regularization. Synthetic examples show that this strategy leads to a better recovery of the underlying medium when low-frequency data is missing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".