3D Nonlinear Ground‐Motion Simulation Using a Physics‐Based Method for the Kinburn Basin
Bibliographic record
Abstract
Abstract We used a finite‐difference modeling method, developed by Olsen–Day–Cui, to simulate nonlinear‐viscoelastic basin effects in a spectral frequency range of 0.1–1 Hz in the Kinburn bedrock topographic basin, Ottawa, Canada, for large earthquakes. The geotechnical and geological features of the study area are unique: loose, postglacial sediments with very low shear‐wave velocities (<200 m/s) overlying very firm bedrock with high shear‐wave velocities (>2000 m/s). Comparing records and simulated velocity time series showed regular viscoelastic simulations could model the ground motions at the rock and soil sites in the Kinburn basin for the Ladysmith earthquake, a local earthquake occurred on 17 May 2013 with Mw 4.7 (MN 5.2). The Ladysmith earthquake was scaled to provide a strong level of shaking for investigating the nonlinear behavior of soil; therefore, a new nonlinear‐viscoelastic subroutine was introduced to the program. A modeled stress–strain relationship associated with ground‐motion modeling in the Kinburn basin using a scaled Ladysmith earthquake event of Mw 7.5 followed Masing’s rules. Using nonlinear‐viscoelastic ground‐motion simulations significantly reduced the amplitude of the horizontal component of the Fourier spectrum at low frequencies and the predicted peak ground acceleration and peak ground velocity values compared with regular linear viscoelastic simulations; hence, the lower soil amplification of seismic waves and the frequency and amplitude spectral content were altered by the nonlinear soil behavior. In addition, using a finite‐fault model to simulate an earthquake with Mw 7.5 was necessary to predict the higher levels of stresses and strains, which were generated in the basin. Using a finite‐fault source for the nonlinear‐viscoelastic simulation caused decreases in the horizontal components because of the shear modulus reduction and increase of damping.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".