Estimation of surface-wave group velocity using slant stack in the generalized S-transform domain
Bibliographic record
Abstract
ABSTRACT We have developed the slant stacking approach in the generalized S-transform (GST) domain of seismic data for the estimation of the group velocity of the multimodal surface wave. We used two versions of the GST. The constant-scale GST uses a constant scaling factor in the Gaussian window to control the spectral time-frequency localization. We found that a smaller scaling factor should be chosen for the low-frequency surface wave, whereas for higher frequencies, a larger scaling factor should be chosen. Therefore, the transform exhibits a trade-off in the group velocity resolution of low frequencies with small values of the scaling factor and the group velocity resolution of high frequencies with large values of the scaling factor. The modified S-transform (MST), another version of the GST used in this study, enhanced the time-frequency resolution by projecting the frequency into a linear frequency function in the Gaussian window. This property allowed us to estimate the group velocity for a broad range of frequencies. We demonstrated the robustness of the MST for the estimation of the group velocity by synthetic and real data examples.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".