Time‐evolving seismic tomography: The method and its application to the 1989 Loma Prieta and 2014 South Napa earthquake area, California
Bibliographic record
Abstract
Abstract We propose a time‐evolving approach to conduct traveltime seismic tomography in the 1989 M w 6.9 Loma Prieta earthquake and 2014 M w 6.0 South Napa earthquake area, California. The recording period of the chosen seismic data between 1 January 1967 and the day before the 2014 South Napa earthquake is divided into two time windows, separated by the 1989 Loma Prieta earthquake. In each time window the subsurface velocity structure is iteratively updated. Starting from the final model of the first time window, the velocity model has been successively improved throughout iterations in the second time window, indicating that the traveltime data of later time windows have provided extra information to refine the subsurface images. Strong heterogeneities are observed in the final P wave velocity model. Both of the two large earthquakes occurred at transition zones in between high V P and low V P anomalies. In all, this study shows the effectiveness of the time‐evolving seismic tomography method.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".