Nucleation Mechanism and Rupture Dynamics of Laboratory Earthquakes at Different Loading Rates
Bibliographic record
Abstract
The loading rate of tectonic stress is not constant during long-term geotectonic activity and significantly affects the earthquake nucleation and fault rupture process. However, the mechanism underlying the loading rate effect is still unclear. In this study, we conducted a series of experiments to explore the effect of the loading rate on earthquake nucleation and stick–slip characteristics. Through lab experiments, faults were biaxially loaded at varying rates to produce a series of earthquakes (stick–slip events). Both shear strain and fault displacement were monitored during these events. The findings indicate a substantial effect of the loading rate on the recurrence interval and the shear stress drop of these stick–slip events, with the recurrence interval inversely proportional to the loading rate. The peak friction of the fault also decreases with the increasing loading rate. Notably, prior to the dynamic rupture of earthquakes, there exists a stable nucleation phase where slip occurs in a quasi-static manner. The critical nucleation length, or the distance required before the dynamic rupture, diminishes with both the loading rate and normal stress. A theoretical model is introduced to rationalize these observations. However, the rupture velocity of these lab-simulated earthquakes showed no significant correlation with the loading rate. Overall, this study enhanced our comprehension of earthquake nucleation and rupture dynamics in diverse tectonic settings.
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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 | 0.000 | 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) | 0.001 | 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".