Multi‐method site characterization to verify the hard rock (Site Class A) assumption at 25 seismograph stations across Eastern Canada
Bibliographic record
Abstract
Site characterization is a crucial component in assessing seismic hazard, typically involving in situ shear‐wave velocity ( V S ) depth profiling, and measurement of site amplification including site period. Noninvasive methods are ideal for soil sites and become challenging in terms of field logistics and interpretation in more complex geologic settings including rock sites. Multiple noninvasive active‐ and passive‐seismic techniques are applied at 25 seismograph stations across Eastern Canada. It is typically assumed that these stations are installed on hard rock. We investigate which site characterization methods are suitable at rock sites as well as confirm the hard rock assumption by providing V S profiles. Active‐source compression‐wave refraction and surface wave array techniques consistently provide velocity measurements at rock sites; passive‐source array testing is less consistent but it is our most suitable method in constraining the rock V S . Bayesian inversion of Rayleigh wave dispersion curves provides quantitative uncertainty in the rock V S . We succeed in estimating rock V S at 16 stations, with constrained rock V S estimates at 7 stations that are consistent with previous estimates for Precambrian and Paleozoic rock types. The National Building Code of Canada uses solely the time‐averaged shear‐wave velocity of the upper 30 m ( V S 30 ) to classify rock sites. We determine a mean V S 30 of ∼ 1600 m/s for 16 Eastern Canada stations; the hard rock assumption is correct (>1500 m/s) but not as hard as often assumed (∼2000 m/s). Mean variability in V S 30 is ∼400 m/s and can lead to softer rock classifications, in particular, for Paleozoic rock types with lower average rock V S near the hard/soft rock boundary. Microtremor and earthquake horizontal‐to‐vertical spectral ratios are obtained and provide site period classifications as an alternative to V S 30 .
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".