Fluid-induced Earthquake Nucleation on Aging Rate-and-State Faults: Influence of Hydraulic Diffusivity and Injection Rate under Different Nucleation Regimes
Bibliographic record
Abstract
Earthquake nucleation length, a critical parameter characterizing the transition from quasi-static propagation to dynamic rupture in the nucleation zone, has been observed to decrease with elevated shear stress loading rate. Recent laboratory experiments suggested that injection can also act as a loading condition, with the nucleation length shortening under high-rate injection. In this study, we performed numerical simulations to investigate how hydraulic diffusivity and injection rate affect the nucleation length of injection-induced seismicity on (aging) rate-and-state faults. Similar to tectonic earthquakes, the nucleation process of injection-induced seismicity falls into two distinct nucleation regimes —no-healing and constant-weakening—defined by the ratio of the weakening to healing rates at the center of the nucleation zone (\(\Omega_{\text{c}}\)). The nucleation length is generally much larger in the constant-weakening regime. Interestingly, we found that the effects of injection rate and hydraulic diffusivity on nucleation length depend on the nucleation regime. In the no-healing regime (\(\Omega_{\text{c}}\gg 1\)), the nucleation length decreases with increasing injection rate or decreasing hydraulic diffusivity. In contrast, within the constant-weakening regime (\(\Omega_{\text{c}}\cong 1\)), the nucleation length displays an opposite trend in most cases. The contrasting behavior can be attributed to differences in the weakening processes within each regime and the timing of the transition from no-healing to constant-weakening. We discussed the underlying mechanisms driving local fault response to changes in hydraulic diffusivity and injection rate, as well as the implications of these findings for field- and lab-scale injection-induced ruptures.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".