The Role of Fluid Pressure‐Induced Aseismic Slip in Earthquake Cycle Modulation
Bibliographic record
Abstract
Abstract The evolving state of fault stress during and after the perturbation of fluid pressure gives rise to an intriguing interplay of seismic and aseismic slip on the fault. A better understanding of the possible role of fluids in the triggering mechanism of seismicity is pivotal to effective seismic hazard mitigation, particularly in the context of induced seismicity. Through numerical modeling, we investigate the effect of pore pressure perturbations on the spatio‐temporal evolution of fault slip and the modulation of earthquake cycles. Pressure perturbations are imposed at different magnitudes and different times during a selected interseismic period. Results show a wide range of aseismic responses which can lead to both time advancement and delay of subsequent earthquakes. Specifically, even pressure perturbation <5% of the average event stress drop can trigger aseismic slip that leads to considerable time delay in the next earthquake even when perturbation occurs late in the interseismic period. We find that earthquakes that are delayed in time are associated with large aseismic moment release. Our study highlights the importance of close monitoring of aseismic fault slip in regions prone to the influence of pore fluids and provides physical insights into identifying critical aseismic responses associated with certain triggering outcomes.
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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".