Probabilistic seismic-hazard site assessment in Kitimat, British Columbia, from Bayesian inversion of surface-wave dispersion
Bibliographic record
Abstract
This paper applies rigorous quantitative inversion methods to estimate seismic-hazard site classification and amplification factors in Kitimat, British Columbia, due to near-surface geophysical conditions. Frequency-wavenumber seismic-array processing is applied to passive data collected at three sites in Kitimat to estimate surface-wave dispersion. The dispersion data are inverted using a fully nonlinear Bayesian (probabilistic) inference methodology to estimate shear-wave velocity (V S ) profiles and uncertainties. The V S results are used to calculate the travel-time average of V S to 30 m depth (V S30 ) as a representation of the average sediment conditions, and to determine seismic-hazard site classification with respect to the National Building Code of Canada. In addition, V S30 -dependent site amplification factors are computed to estimate site amplification at the three Kitimat sites. Lastly, the V S profiles are used to compute amplification and resonance spectra for horizontally polarized shear waves. Quantitative uncertainties are estimated for all seismic-hazard estimates from the probabilistic V S structure. The Kitimat region is the site for several proposed large-scale industrial development projects. One of the sites considered in this study is co-located with a recently deployed soil seismographic monitoring station that is currently recording ground motions as part of a 5 year campaign. The findings from this work will be useful for mitigating seismic amplification and resonance hazards on critical infrastructure, as well as for future seismological research, in this environmentally and economically significant region of Canada.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".