Simplified Bayesian Ground Motion Models for Cumulative Absolute Velocity in Central and Eastern North America
Bibliographic record
Abstract
Cumulative absolute velocity (CAV) has recently emerged as a useful intensity measure (IM) for predicting the occurrence of liquefaction and its consequences, including foundation settlement. However, few ground motion models for CAV exist that are applicable in Central and Eastern North America (CENA). The relative paucity of strong motion data for this region and tectonic setting, particularly for large earthquakes and short distances to rupture, is the primary challenge hindering the development of such models. This study applies a Bayesian approach to develop a ground motion model for CAV in CENA. This approach consists of first developing a model using a large database (drawn from the NGA-West2 database), then updating the coefficients in light of observations from a smaller database which is specific to the region of interest (drawn from the NGA-East database). The models developed using the Bayesian approach are compared with using the same functional form in a traditional regression strategy with the NGA-East data, as well as with the model regressed with the NGA-West2 data and with other models in the literature. The Bayesian approach prevents overfitting in the NGA-East data, where few records are available for large magnitude earthquakes at short distances. The use of NGA-West2 data to constrain development of models for CENA follows existing studies that use NGA-West2 models as a baseline and develop adjustments to make the models applicable in a different region and tectonic setting. The Bayesian approach proposed in this study is also applicable for developing other region-specific ground motion models for regions that lack data compared to regions such as California, New Zealand, and Japan that have relatively rich data available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".