Sensitivity Analysis for Finite‐Difference Seismic Basin Modeling: A Case Study for Kinburn Basin, Ottawa, Canada
Bibliographic record
Abstract
Abstract We used a finite‐difference modeling method to investigate the sensitivity of the ground‐motion simulation results to the main input parameters, including the source model, regional path properties, and local site conditions. We used a spectral frequency range of 0.1–1 Hz for the Kinburn bedrock topographic basin, Ottawa, Canada, for the Ladysmith earthquake (Mw 4.7). Some findings are known facts; however, the unique geophysical conditions in the Ottawa area, such as the high contrast between the shear‐wave velocities of the bedrock and the shear‐wave velocity of the soil, were the reason for a comprehensive sensitivity analysis. Using a Gaussian source function with a short half‐duration increased the peak ground velocities (PGVs) and the amplitude of the velocity Fourier spectrum. Relaxation times and relaxation coefficients for the viscoelastic simulation significantly increased the amplitude of later arrivals at the soil site, which, consequently, led to an increase in PGV, the amplitude of the pseudospectral acceleration (PSA) ratio, and the velocity Fourier spectrum for a small earthquake. Employing a small soil Q model damped a significant amount of energy of the waves in the basin; thus, PGV, the PSA of soil to rock ratios, and the velocity Fourier spectrum were dependent on the soil Q model. Also, using a high‐velocity contrast between soil and rock increased PGVs and the amplitude of the PSA of the soil to rock ratios, whereas the frequency content of the waves shifted toward lower frequencies. Using a finite‐fault source model for a scenario large earthquake (Mw 7) significantly reduced the PGV values relative to a point‐source model. Using nonlinear‐viscoelastic simulation for a large earthquake (Mw 7) reduced the amplitude of the later arrivals and the amplitude of the PSA of the soil to rock ratios, and shifted the frequency content of waves toward lower frequency.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".