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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".