Novel first-order <i>k</i> -space formulations for wave propagation by asymmetrical factorization of space-wavenumber domain wave propagators
Bibliographic record
Abstract
ABSTRACT Wave-equation simulation based on the k-space method produces nearly dispersion-free wavefields and enhances simulation stability. However, for simulation in heterogeneous media, the conventional first-order k-space method requires many mixed-domain operators, which are the most expensive part of the wave-extrapolation process. We have analyzed and summarized the problem of the conventional k-space method as symmetrical factorization of the wave propagators. Based on this analysis, we develop a novel asymmetrical factorization-based k-space method that can significantly reduce the number of mixed-domain operators without compromising modeling accuracy. By using this method, the number of mixed-domain operators is reduced by half, and thus, the computational cost decreases significantly. Furthermore, we have compared our method to the conventional pseudospectral method. The comparison finds that, at comparable accuracy, our method is more efficient due to its ability to use a larger time step. Acoustic and elastic examples demonstrate the correctness and effectiveness of our method.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".