Propagation of a fluid-induced aseismic crack leads to earthquake swarm migration controlled by fluid volume
Bibliographic record
Abstract
Seismicity migration is one of the most remarkable features of earthquake swarms because of its ubiquity and the wide range of migration durations, velocities and shapes observed. The dynamic properties of swarms, like seismic moment or number of events, are often attributed to fluid circulation, directly or indirectly. However, classical models of fluid pressure diffusion aiming at explaining seismicity triggering and migration show some limitations. An increasing body of evidence points at an important contribution of fluid-induced aseismic slip during swarms. Moreover, parameters like injection history and fault criticality are expected to intervene. In this work, we use a fracture mechanics framework to show that earthquake migration can be explained as driven by the propagation of a fluid-induced aseismic slip transient on a rate-and-state fault. This theoretical model predicts a simple linear relation between the seismic migration distance and the square root of the injected fluid volume. This relation is validated by observations in two well-studied seismic sequences induced by injections for geothermal purposes (Basel and Soultz-sous-Forêts), in which the seismicity is mainly clustered around a single surface. In addition, the model helps constrain frictional, hydraulic and structural properties of the fault hosting aseismic slip, and can be reasonably generalized to all fluid-induced earthquake swarms, natural and anthropogenic.
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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 | 0.000 |
| 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".