Uncertainty of linear earthquake site amplification via Bayesian inversion of surface seismic data
Bibliographic record
Abstract
ABSTRACT We examine uncertainty in predicted linear 1D site amplification due to uncertainty in shear-wave velocity (VS) structure quantified from Bayesian (probabilistic) inversion of microtremor array dispersion data. Based on a sample of VS profiles drawn from the posterior probability density of the microtremor inversion, probability distributions are computed for common predictors of site amplification including VSZ (traveltime average VS to a depth z) and amplification spectra based on seismic impedance variations and full transverse shear-wave effects. These methods are applicable for any site, but the resulting probabilistic site amplification analyses are specific to the two sediment sites studied here with strongly contrasting geology in high population centers of British Columbia, Canada. The site amplification probability distributions for the two sites are shown to be more informative than amplification estimated for a single best-fit VS profile by characterizing the uncertainty and therefore level of confidence in the predictions. The shear-wave amplification probability spectra are evaluated by comparison to empirical earthquake and microtremor spectral ratios, with generally good agreement in resonant peak frequencies and amplification levels, providing confidence that the primary influence of site-specific structure is accounted for appropriately. The wider implication here is that proper characterization of the VS(z) profile uncertainty distribution from inversion of cost-effective surface wave dispersion data is beneficial in the application of said profiles to the prediction of earthquake site response and its uncertainty, as required for probabilistic seismic hazard assessment.
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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) | 0.001 | 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 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".