Real‐Time Earthquake Location Based on the Kalman Filter Formulation
Bibliographic record
Abstract
Abstract Seismic location is an essential task for earthquake monitoring. The general practice is to locate earthquakes using arrival times from all recorded stations. However, this is not well suited for real‐time applications such as the earthquake early warning, where earthquake locations need to be determined and updated as more stations are triggered. Here we have developed a real‐time linear location method based on a Kalman filter formulation. It updates location and its uncertainty whenever a station is triggered. We have demonstrated its effectiveness with synthetic and real data sets in Parkfield, California in a retrospective mode. The tests show that we can obtain relatively accurate locations and reliable uncertainties for earthquakes with 4 or 5 stations triggered. In particular, without considering the station latency, accurate earthquake locations can be achieved retrospectively in about 3.3 and 2.7 s for the 2003 San Simeon Mw6.6 earthquake and 2004 Parkfield Mw6.0 earthquake, respectively.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".