Fast and full characterization of large earthquakes from prompt elastogravity signals
Bibliographic record
Abstract
Prompt ElastoGravity Signals (PEGS) are light-speed gravity-induced signals recorded by seismometers before the arrival of seismic waves. They have raised interest for early warning applications but their weak amplitudes, close to the background seismic noise even for large earthquakes, have questioned PEGS actual potential for operational use. A deep-learning model has recently demonstrated its ability to mitigate this noise limitation and to provide in near real-time the earthquake moment magnitude (Mw). However, this approach has proven to be efficient only for very large earthquakes (Mw > 8.3) of known focal mechanism. Here we show unprecedented performance in full earthquake characterization from PEGS using the dense broadband seismic network deployed in Alaska and Western Canada. Our deep-learning model is able to provide accurate magnitude and focal mechanism estimates of Mw > 7.8 earthquakes, 2 minutes after origin time (hence the tsunamigenic potential). For very large earthquakes whose rupture is still ongoing after 2 minutes, the model tracks the instantaneous magnitude from that time until the rupture completion. Our results represent a major step towards the routine use of PEGS in operational warning systems, and demonstrate its potential for tsunami warning in the Alaska region, and other densely-instrumented areas.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".