Probabilistic Seismic Hazard Assessment for the North China Plain Earthquake Belt: Sensitivity of Seismic Source Models and Ground Motion Prediction Equations
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In this study, a multi-source data fusion method was proposed for the development of a Hybrid seismic hazard model (HSHM) in China by using publicly available data of the 5th Seismic Ground Motion Parameter Zoning Map (NSGM) and historical seismic catalogues and integrating with modern ground motion prediction equations (GMPEs). This model incorporates the characteristics of smoothed seismicity and areal sources for regional seismic hazard assessment. The probabilistic seismic hazard for the North China Plain earthquake belt was investigated through sensitivity analysis related to the seismicity model and GMPEs. The analysis results indicate that the Hybrid model can produce a consistent result with the NSGM model in many cases. However, the NSGM model tends to overestimate hazard values in locations where no major events have occurred and underestimate hazard values in locations where major events have occurred. The Hybrid model can mitigate the degree of such biases. Compared to the modern GMPEs, the GMPE with epicentral distance measures significantly underestimate the seismic hazard under near-field and large-magnitude scenarios. In addition, a comparison of the uniform hazard spectra (UHS) obtained by the models, with China’s design spectrum, shows that the current design spectrum is more conservative than the calculated UHS.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it