Seismological Ground-Motion Models for Generic Hard-Rock Site Condition in Western Canada
Bibliographic record
Abstract
ABSTRACT We develop ground-motion models (GMMs) to characterize the Fourier amplitude spectrum (FAS) for earthquakes in British Columbia (B.C.), Canada. GMMs developed for FAS are useful for understanding the underlying seismological parameters and can be transformed into GMMs for response spectral values for other applications (e.g., probabilistic seismic hazard analysis [PSHA]). The GMMs are calibrated using a compiled FAS database, referenced to a B.C. generic hard-rock site condition (shear-wave velocity ∼2285 m/s). The GMMs are developed separately for crustal, in-slab, transition, offshore, and Haida Gwaii events. The GMMs are calibrated using data mostly from M 2.5 to 5.5 earthquakes recorded at rupture distances of ∼50 to 500 km. Outside this range, the models are constrained by a seismological model supplemented with sparse observational data. The amplitude decay rate for crustal earthquakes in B.C. is very similar to that given by the empirical model of Bayless and Abrahamson (2018; hereafter, BA18), developed from California data on soil sites. However, we observe magnitude- and frequency-dependent differences between the models for ground-motion amplitude levels. We attribute these to (1) the different reference-site conditions of the models, with the B.C. GMMs being referenced to hard rock, and (2) steeper magnitude scaling at small-to-moderate magnitudes for events in B.C. in comparison to the BA18 model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".