Rapid identification of earthquake rupture plane using Source-Scanning Algorithm
Bibliographic record
Abstract
Accurate identification of an earthquake's rupture plane is critical in many aspects, ranging from scientific, such as seismology and plate tectonics, to societal, such as emergency rescue planning. We modify the recently developed Source‐Scanning Algorithm (SSA) to rapidly image the rupture pattern of an earthquake using waveform data recorded at local and regional distances. The method calculates the brightness function of a grid point by summing the observed amplitudes of the P wave envelopes at the corresponding predicted arrival times at all stations. The composite image, consisting of brightness functions of all grid points within a prescribed source volume and time interval, illuminates the locations on the rupture plane where significant seismic energy is emitted. Synthetic tests indicate that the proposed method is robust even under the presence of hypocentral mislocation and origin time errors. Application of this method to the 2003 San Simeon and 2004 Parkfield earthquakes (both occurred in central California) clearly identifies the NW–SE‐striking planes as the actual rupture planes, a conclusion consistent with all available evidences. Limited azimuthal distribution of seismic stations will not severely affect the identification result, making this method ideal for offshore earthquake studies. Because the proposed method is able to identify the rupture plane using seismic waveform data only, it is perfectly suited for near‐real‐time operation and may enable routine report of earthquake rupture planes by local seismic networks in the future.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".