Efficient full waveform inversion in the time-ray parameter domain using iteration-dependent sets of ray parameters
Bibliographic record
Abstract
Full waveform inversion (FWI), which has been widely studied in recent years, still cannot be practiced in industry effectively. The reasons for this include computational cost, large memory requirements, slow convergence rate, cycle skipping, etc. In our implementation, a linear source encoding strategy is used for the gradient calculation in time-ray parameter domain. The plane wave encoding approach forms super-gathers by summing densely distributed individual shots, and can reduce the computational burden considerably. We also construct the diagonal part of the pseudo-Hessian using a hybrid source encoding method. The diagonal part of the encoded pseudo-Hessian is a good approximation to the full Hessian matrix, which preconditions the gradient. The preconditioning is equivalent applying a deconvolution imaging condition in prestack reverse time migration. To avoid the local minimum problem, a multi-scale approach in the time domain is employed, by (1) applying a low-pass filtering to the data residuals and (2) increasing the frequency bands step by step. This has been proved to be effective against cycle skipping. We assemble this suite of tools and carry out a numerical experiment with a modified Marmousi model, analyzing the effectiveness of this combination of strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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".