A time‐frequency dependent coherence model for seismic ground motions
Bibliographic record
Abstract
Abstract There are several well‐known empirical lagged spatial coherence models for seismic ground motions proposed in the literature. The models are often developed based on the ordinary Fourier transform. None of the parametric models depend on time and frequency. The present study is focused on the development of the time‐frequency dependent (TF‐dependent) lagged coherence model for the seismic ground motions. The estimation of the TF‐dependent lagged coherence is carried out using the records obtained from dense arrays in Taiwan by applying the S‐transform—a TF‐dependent windowed Fourier transform. The spectral analysis results show that the TF‐dependent lagged coherence decreases with increasing separation or increasing frequency. Most importantly, it is shown that the TF‐dependent lagged coherence varies with the time‐varying intensity within the duration of the records; a higher normalized intensity corresponds to a higher lagged coherence. This feature is included in the developed empirical parametric TF‐dependent lagged coherence model, which is a function of the frequency, the separation between recording sites, and the normalized intensity. A numerical example illustrating its application to simulate nonstationary ground motions at multiple points is presented by using the time‐frequency spectral representation method that was developed based on the S‐transform and discrete orthonormal S‐transform.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".