Non‐ergodic correction factors for hard rock sites in Western Canada
Bibliographic record
Abstract
For short‐period structures situated on hard rock, accurate characterization of ground motions at high frequencies is critical. Predictions based on empirical ground motion models (GMMs) for generic site conditions carry large uncertainties, especially because the datasets used to develop GMMs are often lacking in observations for hard rock sites. The current state‐of‐practice is to perform a V S ‐ host‐to‐target correction, where V S is the site shear‐wave velocity and is the near‐surface high‐frequency attenuation parameter. However, in the absence of site‐specific ground motion data, the modeling parameters ( V S and ) and their reliability in predicting the correct adjustment factors are not well quantified and are subject to large epistemic uncertainty. We apply an alternative framework to characterize hard rock site response in probabilistic seismic hazard analysis (PSHA) using site‐specific ground motion data. Our methodology relies on estimation of the non‐ergodic hard rock site response at a given site, as derived using site‐specific Fourier amplitude spectra. The use of site‐specific data allows us to remove the site‐to‐site variability component from PSHA and greatly reduce epistemic uncertainty of the hard rock site response. We demonstrate the applicability of the proposed methodology for a critical facility in British Columbia (B.C.), Canada. Comparison of the non‐ergodic and generic hard rock correction factors reveals the importance of compiling and using site‐specific data in PSHA. For competent hard rock, we suggest that amplification at high frequencies is often controlled by factors other than impedance effects, such as topographic effects or near‐surface high‐frequency attenuation; such effects are not well modeled through the traditional V S ‐ approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".