Identification of Repeating Earthquakes: Controversy and Rectification
Bibliographic record
Abstract
Abstract Repeating earthquakes (repeaters) are events that recurrently rupture the same fault patch with nearly identical magnitudes. Although repeaters have been widely studied and utilized in many fields over the last four decades, there are no standard criteria for reliably identifying such events. The current criteria adopted in the geophysical research community are inconsistent and difficult to justify. Different criteria may inescapably incur inadequate hypotheses and lead to controversial interpretations, highlighting the urgent need for seeking a uniform approach to reliably identify repeaters. In this study, we address this long-standing issue by deriving the most logical criteria on the basis of theoretical calculation with simple yet reasonable assumptions. Quantitatively, we define a repeating pair if their interevent distance is ≤80% of the rupture area of the larger event and their magnitude difference is ≤0.3. We demonstrate the superiority of our proposed approach with challenging cases in California, and our results shed new insight into the hierarchical fault structures in the source areas. Although this study focuses on defining repeating earthquakes, the application to repeating seismic events in other planetary bodies such as moonquakes and marsquakes is straightforward, potentially help avoid misinterpretations of the physical processes in both Earth and planetary interiors.
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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 | 0.028 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".