Referenced Empirical Ground-Motion Model for Eastern North America
Bibliographic record
Abstract
ABSTRACT We update ground‐motion prediction equations (GMPEs) for eastern North America (ENA) using the referenced empirical approach of Atkinson (2008). The technique is based on the use of residual analysis that models differences between regional ground‐motion observations and a reference GMPE developed for a data‐rich region. The update is timely because the Next Generation Attenuation‐West 2 GMPEs for shallow crustal earthquakes in active tectonic regions enable a significant improvement in the implementation of this model, relative to previous work (e.g., Atkinson and Boore, 2011). The predicted ground‐motion amplitudes of the ENA referenced empirical model are very similar to the equivalent California values of Boore et al. (2014; hereafter BSSA14) at close distances ( R ≤50 km), at low‐to‐moderate frequencies ( f ≤5 Hz). At regional distances ( R >50 km) and at high frequencies ( f >5 Hz), the ENA data suggest higher ground‐motion amplitudes than the BSSA14 reference model, presumably due to lower attenuation and higher stress for ENA events relative to those in active tectonic regions. We also show that the referenced empirical approach predicts ground motions that are consistent with those that would be produced by the hybrid empirical approach (Campbell, 2003), considering recent equivalent point‐source models that match both ENA and California ground‐motion databases. Comparison between the referenced empirical GMPE of this study (HA14) and the stochastic GMPE of Atkinson and Boore (2006, 2011; denoted AB06′) shows that both models imply similar attenuation shape at all frequencies. For M 7 at R ≤50 km, the HA14 model predicts relatively smaller ground‐motion amplitudes than does the AB06′ model, likely because of the greater saturation effects in the empirical BSSA14 reference model.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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".