Ground‐Motion Attenuation Model for Small‐To‐Moderate Shallow Crustal Earthquakes in California and Its Implications on Regionalization of Ground‐Motion Prediction Models
Bibliographic record
Abstract
This paper presents the development of a ground‐motion prediction model for small‐to‐moderate shallow crustal earthquakes (3⩽ M ⩽5.5, up to 200 km distance) using data from the California ShakeMap systems. Our goal is to provide an empirical model that can be confidently used in the investigation of ground‐motion difference between California and other active tectonic regions (such as the Pacific Northwest and British Columbia, Canada) where the bulk of ground‐motion data from shallow crustal earthquakes is in the small‐to‐moderate magnitude range. This attenuation model is developed as a small‐magnitude extension of the Chiou and Youngs NGA model (CY2008). We observe, and incorporate into this model, a regional difference in median amplitude between central and southern California earthquakes. The strength of the regional difference diminishes with increasing spectral period. More importantly, it is magnitude dependent and becomes insignificant for M ⩾6 earthquakes, as indicated by the large‐magnitude California data used in CY2008. Together, these findings have important implications on the practice of utilizing the regional differences observed in small‐to‐moderate earthquakes to infer the regional differences expected in large earthquakes, including the NGA model applicability in active tectonic regions outside California.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".