Do the statistical properties of aftershocks change in fluid-induced settings?
Bibliographic record
Abstract
Fluid-induced earthquakes are an adverse effect of industrial operations like hydraulic fracturing (e.g., 4.7 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. In particular, the statistical properties including the productivity relation and the Omori-Utsu relation appear to hold for fluid-induced seismicity as well. Here, we investigate the underlying potential cause of these field observations from a modelling perspective. By extending a novel conceptual model by integrating (non-)linear viscoelastic effects with a combination of fluid diffusion and invasion percolation associated with a point source, we are able to capture the essential characteristics of crustal rheology and stress interactions in a porous medium. We show that this gives rise to realistic aftershock behaviour with statistical properties indistinguishable from the case of seismicity resulting from tectonic loading. This is even true if the loading due to fluid injections occurs at time scales much faster than the tectonic loading. In our model framework, such tectonic loading can be mimicked by a spatially uniform drive replacing the point source of the fluid injection and its propagation to initiate slips and earthquakes. This indicates that the emergence of the Omori-Utsu relation is independent of how the system is loaded or driven and it is indeed only controlled by the viscoelasticity of the medium. Similarly, the scaling exponent of the productivity relation --- which quantifies how the number of aftershocks increases with the magnitude of the main shock --- is independent of how the system is driven. At the same time, the spatial footprint of fluid-induced events and its dependence on the permeability field are primarily unaltered by the presence of aftershocks. Finally, within our model framework, we systematically investigate the impact of varying fluid injection rates during the viscoelastic stress redistribution on the detection of aftershocks and event-event triggering sequences. When the injection rate is sufficiently high, the aftershock detection and recovery of the Omori-Utsu and productivity relations is only feasible when the internal stress redistribution is directly accessible.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".