Real-Time Characterization of Finite Rupture and Its Implication for Earthquake Early Warning: Application of FinDer to Existing and Planned Stations in Southwest China
Why this work is in the frame
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Bibliographic record
Abstract
Earthquake early warning (EEW) not only improves resilience against the risk of earthquake disasters, but also provides new insights into seismological processes. The Fin ite-Fault Rupture De tecto r (FinDer) is an efficient algorithm to retrieve line-source models of an ongoing earthquake from seismic real-time data. In this study, we test the performance of FinDer in the Sichuan-Yunnan region (98.5 o E–106.0 o E, 22.0 o N–34.0 o N) of China for two datasets: the first consists of seismic broadband and strong-motion records of 58 earthquakes with 5.0 ≤ M S ≤ 8.0; the second comprises additional waveform simulations at sites where new stations will be deployed in the near future. We utilize observed waveforms to optimize the simulation approach to generate ground-motion time series. For both datasets the resulting FinDer line-source models agree well with the reported epicenters, focal mechanisms, and finite-source models, while they are computed faster compared to what traditional methods can achieve. Based on these outputs, we determine a theoretical relation that can predict for which magnitudes and station densities FinDer is expected to trigger, assuming that at least three neighboring stations must have recorded accelerations of 4.6 cm/s 2 or more. We find that FinDer likely triggers and sends out a report, if the average distance between the epicenter and the three closest stations, D epi , is equal or smaller than log 10 ( M a + b ) + c, where a = 1.91, b = 5.93, and c = 2.34 for M = M W ≥ 4.8, and c = 2.49 for M = M S ≥ 5.0, respectively. If the data used in this study had been available in real-time, 40–70% of sites experiencing seismic intensities of V-VIII (on both Chinese and MMI scales) and 20% experiencing IX-X could have been issued a warning 5–10 s before the S- wave arrives. Our offline tests provide a useful reference for the planned installation of FinDer in the nationwide EEW system of Chinese mainland.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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