Methodology for Site Classification Estimation Using Strong Ground Motion Data from the Chi‐Chi, Taiwan, Earthquake
Bibliographic record
Abstract
The ground motions of the Chi‐Chi, Taiwan, earthquake ( M w =7.6) were recorded at 420 strong‐motion stations, including 69 near‐fault sites. However, the site conditions of many stations are not available. Among 420 strong‐motion stations, the site conditions are known for only 87 stations, which were classified into four groups ( S 1 , S 2 , S 3 , and S 4 ) by using borehole data and some surface geology. This paper presents a methodology to estimate the missing site condition information at strong‐motion stations in Taiwan. The method is based on the shape of the 5% damped pseudo‐acceleration spectrum of the horizontal ground motion component normalized with respect to average PGA, where the classification scheme is developed using the data from the 87 stations for which the site conditions are known. Possible effects of soil nonlinearity, and distance to the fault on the classification are investigated. The results obtained from the proposed methodology are well correlated with the available known site classification information data. The methodology is then applied to estimate the site condition for the other 333 stations without known site classification. Our results are compared to previous results obtained based on interpretation of geologic maps and geomorphologic data. We find that the two approaches agree in 71% of the cases. We also tested the horizontal‐to‐vertical spectral ratio technique to estimate the site classification of other 333 strong‐motion stations. However, this technique resulted in lower accuracy than does the proposed technique based on the spectral shape of normalized response spectra.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".