Three dimensional nonlinear simulation, sensitivity analysis and increasing the maximum frequency to 2.5 Hz using a physics-based method for basins in Ottawa, Canada
Bibliographic record
Abstract
We performed 3-D nonlinear-viscoelastic ground motion simulations using a finite difference modeling method in a frequency range of 0.1 to 1 Hz in the Kinburn basin, Ottawa, Canada, for large earthquakes. Comparing the records and simulated velocity time series showed that regular viscoelastic simulations could model the ground motions at the rock and soil sites in the Kinburn basin for the Ladysmith earthquake (Mw=4.7). Using nonlinear-viscoelastic ground motion simulations for the scaled Ladysmith earthquake (Mw=7.5) significantly reduced the amplitude of the horizontal components of the Fourier spectrum and the predicted PGA and PGV values compared to regular linear viscoelastic simulations. Further, using a finite fault source (Mw=7) for the nonlinear-viscoelastic simulation decreased PGAs of the horizontal components. Our sensitivity analysis of simulations for different seismic moments showed that the PGV values exponentially increased with moment magnitude. Using a Gaussian source function with a short half duration increased the PGVs and the amplitude of velocity Fourier spectrum. Relaxation times and relaxation coefficients for viscoelastic simulation significantly increased PGV, the amplitude of the PSA ratio, and the velocity Fourier spectrum for a small earthquake. Employing a small soil Q model reduced PGV, PSA of soil/rock ratios, and the amplitude of velocity Fourier spectrum. Using finite fault model for a large earthquake (Mw =7) significantly reduced the PGV values relative to a point source model.We increased the maximum frequency to 2.5 Hz in FD modeling using a dual grid size method for two basins (Kinburn and Orleans basins in Ottawa, Canada). The simulated velocity time series from the dual grid size method provided better results compared to the results of the single grid size, although there were large differences between the amplitude of the velocity Fourier spectrum of the simulations and the amplitude of the records, particularly at low frequencies (<1 Hz). However, the velocity Fourier spectrums of the records and simulations showed a similar variation versus frequency. Further, using the dual grid size method increased the PGVs and amplitudes of the velocity Fourier spectrum for both the Kinburn and Orleans basins compared to the results of the single grid size simulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".