Nucleation and arrest of dynamic slip on a pressurized fault
Bibliographic record
Abstract
Elevated pore pressure can lead to reactivation of slip on pre‐existing fractures and faults when the static Coulomb failure is reached locally. As the pressurized region spreads diffusively, slip can accumulate quasi‐statically (paced by the pore fluid diffusion) or dynamically. In this work, we consider a prestressed fault with a locally peaked, diffusively spreading pore pressure field to study (1) conditions leading to the escalation of slip and nucleation of dynamic rupture and (2) rupture run‐out distance before it is arrested. Nucleation appears in this model when the fault friction decreases from its peak value with slip, while arrest of dynamic propagation is imminent on aseismic faults (i.e., such that prestressτb is less than the residual fault strength τrat ambient conditions). When fluid overpressure is a small‐to‐moderate fraction of the ambient value of normal effective stress (and prestress is large enough for fault slip to be activated by overpressure), dynamic rupture always nucleates, and the nucleation length increases with decreasing prestress practically independently of the overpressure value. Transition from the ultimately unstable (τb > τr) to the ultimately stable (τb < τr) fault loading is marked by a strong increase of the nucleation length (∝1/(τb − τr)2) as τb approaches τr from above. For aseismic faults (τb < τr), no dynamic rupture is nucleated at large fluid overpressures for all but the smallest values of prestress. The largest run‐out distances of dynamic slip on aseismic faults correspond to overpressure/prestress just sufficient for slip activation. In such cases, the dynamically accumulated slip can lead to enhanced, dynamic fault weakening, resulting in a sustained dynamic rupture and generating a large earthquake. This is consistent with field observations when the largest injection‐induced seismicity occurred after fluid injection ended.
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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 | 0.000 |
| 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 | 0.001 |
| 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".