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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".