Applicability of the Site Fundamental Frequency as a<i>V</i><sub><i>S</i>30</sub>Proxy for Central and Eastern North America
Bibliographic record
Abstract
We introduce a new proxy measure for V S 30 (time‐averaged shear‐wave velocity in the upper 30 m) for central and eastern North America (CENA). The new proxy is the site fundamental frequency ( f peak), measured from the horizontal‐to‐vertical (H/V) spectral ratios of recorded ground motion (or ambient noise). In this study, H/V spectral ratios are obtained from 5% damped pseudospectral acceleration (PSA) from seismograph stations in CENA using the Next Generation Attenuation‐East (NGA‐East) database. We correlate the measured V S 30 values at recording stations with the corresponding f peak values to obtain a predictive relationship for V S 30. The uncertainty of the V S 30 estimate using the f peak‐based model is small (0.14log10 units) in comparison to that for the proxy‐based methods (e.g., topographic slope and surface geology proxies) used in the NGA‐East database (0.25log10 units). However, values of f peak can be obtained for only about 23% of the NGA‐East recording stations due to data and site‐type limitations. Reducing the error in V S 30 estimates can potentially reduce the variability of ground‐motion prediction equations (GMPEs). To test this hypothesis, we use the f peak‐based V S 30 estimates and select one of the proponent NGA‐East GMPE models. For a selected database, we are able to reduce the GMPE variability ( σ ) by 3% on average, for PSA at 1–10 Hz, just using the f peak‐based proxy to estimate V S 30. Greater variability reductions could be achieved by replacing V S 30 with improved site characterization parameters, including f peak, in the GMPEs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".