On the Potential Role of Viscoelasticity in Fluid‐Induced Seismicity
Bibliographic record
Abstract
Abstract Fluid‐induced earthquakes adversely affect industrial operations like hydraulic fracturing (e.g., 4.6 Mw in Alberta, Canada) and enhanced geothermal systems (e.g., 5.5 Mw in Pohang, South Korea). Identifying all underlying physical processes contributing to fluid‐induced seismicity presents an open challenge. Recent work reports signatures of event‐event triggering or aftershocks—common for tectonic settings—within the context of fluid‐induced seismicity. Here, we investigate the underlying potential cause of these field observations from a modeling perspective. We extend a novel conceptual model to simulate the characteristics of crustal rheology and stress interactions in a porous medium by combining viscoelastic effects with fluid diffusion and invasion percolation associated with a point source. Our model successfully reproduces realistic aftershock behavior and statistical properties similar to those resulting from tectonic loading indicating that the statistical properties of aftershocks are unaffected by the fluid injection rate. At the same time, the Gutenberg‐Richter relation, the spatial footprint of fluid‐induced events and their dependence on the permeability field are largely unaltered by the viscoelasticity of the medium and the aftershocks it causes. Furthermore, we investigate the impact of varying fluid injection rates on detecting aftershocks and event‐event triggering sequences during viscoelastic stress redistribution. We find that when the injection rate is sufficiently high, aftershock detection and recovery of their statistical properties are only feasible when the underlying internal stress redistribution is directly accessible. This could explain why aftershocks have not been reported in some field studies of fluid‐induced seismicity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".