Identification of Repeating Earthquakes: Misconception of Waveform Similarity and a Physics-based Solution
Bibliographic record
Abstract
Identification of repeating earthquakes (repeaters) usually depends on waveform similarity expressed as the corresponding cross-correlation coefficient (CC) above a prescribed threshold, typically ranging from 0.70 to 0.98. However, the robustness and effectiveness of such a strategy have never been thoroughly examined. In this study, we examine whether CC is a valid proxy for repeater identification through both synthetic and real earthquake experiments. We reveal that CC is controlled by not only the inter-event distance but also many other factors, including station azimuth, epicentral distance, velocity structure, etc. Consequently, CC lacks the resolution in identifying true repeaters. We propose a physics-based approach that considers both inter-event separation and rupture radius. For an event pair to be true repeaters, their inter-event separation must be smaller than the rupture radius of the larger event. Our results imply that a systematic recheck of previously identified repeaters and associated interpretations/hypotheses may be important and necessary.
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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 | 0.001 | 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 | 0.000 | 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 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".