A Ground-Motion Prediction Equation for Fennoscandian Nuclear Installations
Bibliographic record
Abstract
ABSTRACT We propose a ground-motion prediction equation (GMPE) for probabilistic seismic hazard analysis of nuclear installations in Finland. We collected and archived the acceleration recordings of 77 earthquakes from seismic stations on very hard rock (VHR, i.e., the shear-wave velocity in the upper 30 m of the geological profile=2800 m/s according to the definition used in the nuclear industry) in Finland and Sweden since 2006 and computed the corresponding response spectra important for engineering evaluation. We augmented the narrow magnitude range of the local data by a subset of VHR recordings of 33 earthquakes from the Next Generation Attenuation for Central and Eastern North America (CENA) (NGA-East) database, mainly from eastern Canada. We adapted the backbone curves of the G16 equation proposed by Graizer (2016) for CENA. After the calibration, we evaluated the accuracy of the median prediction and the random error. We conclude that the GMPE developed can be used for predicting ground motions in Fennoscandia. Because of compatibility with the original G16 backbone curve and comparisons with the NGA-East GMPEs, we estimate that the formulation proposed is valid on VHR over the range of 2≤moment magnitude≤7.0 and 0≤ rupture distance ≤300 km, the depth range over 1.5–37 km, and frequencies between 1 and 100 Hz. The median of the composite prediction of the GMPE proposed was reasonable. The standard deviation of the prediction error (σ) was over the range of 0.73–0.86, in ln spectral acceleration units, for the relevant spectral frequencies. This is somewhat lower than the G16 σ, indicating lower aleatory variability. The new Fenno-G16 GMPE is applicable over a wider range of magnitudes than the two older GMPEs available in Finland and fits the data better, especially for peak ground acceleration and 25 Hz.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".