Modeling High Stress Drops, Scaling, Interaction, and Irregularity of Repeating Earthquake Sequences Near Parkfield
Bibliographic record
Abstract
Abstract Repeating earthquake sequences have been actively investigated to clarify many aspects of earthquake physics. The two particularly well‐studied sequences, known as the Los Angeles and San Francisco repeaters, have several intriguing observations, including their long (for the seismic moment) recurrence times that would suggest stress drops of 300 MPa based on typical assumptions, near‐syncronized timing prior to 2004, and higher than typical inferred stress drops (of 25 to 65 MPa, up to 90 MPa locally), but not as high as the recurrence times suggest. Here we show that all these observations are self‐consistent, in the sense that they can be reproduced in a single fault model. The suitable models build on the standard rate‐and‐state fault models, with velocity‐weakening patches imbedded into a velocity‐strengthening region, by adding either enhanced dynamic weakening during seismic slip or elevated normal stress on the patches, or both, to allow for the higher stress drops. Such models are able to match the observed average properties of the San Francisco and Los Angeles repeaters, as well as the overall nontrivial scaling between the recurrence time and seismic moment exhibited by many repeating sequences as a whole, for reasonable parameter choices based on experiments and theoretical studies. These models are characterized by the occurrence of substantial and variable aseismic slip at the locations of the repeating sources, which explains their atypical relation between recurrence interval and seismic moment, induces variability in the repeating source properties as observed, and results in their neither slip‐ nor time‐predictable behavior.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".