Interdependent effects of fluid injection parameters on triggered aseismic slip and seismicity
Bibliographic record
Abstract
In the context of fluid-induced seismicity, various injection parameters have been shown to affect fault behaviour differently, although existing studies about their effects sometimes show contradictory results. Aseismic slip is also known to affect seismicity, but its exact contribution remains elusive. To address these, we perform numerical modelling to understand the effects of injection volume and rate on long-term seismic and aseismic fault slip behavior. Our results suggest that both parameters can affect various aspects of fault behaviour to different extents, and, in some cases, their roles are interdependent, thus they should be examined simultaneously in order to fully characterize their effects on triggered fault responses. Within the model space, we observe the fault predominantly releasing aseismic energy, which plays a significant role in altering the timing of triggered earthquakes that follow and exhibits lasting impacts in subsequent seismic cycles. In terms of seismic responses, increasing injection rate enhances the size of the triggered cluster, while increasing injection volume increases seismicity rate of the sequence. Detailed characterization of the patterns of earthquake occurrence and moment release with respect to different injection parameters can offer insights into establishing safe bounds of injection operation and potentially mitigate seismic hazard.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".