Control of Seismicity Migration in Earthquake Swarms by Injected Fluid Volume and Aseismic Crack Propagation
Bibliographic record
Abstract
Abstract The evolution of fluid injection‐induced seismicity, generally characterized through the number of events or their seismic moment, depends on, among other factors, the injected fluid volume. Migration of seismicity is observed during those sequences and might be caused by a range of mechanisms: fluid pressure diffusion, fluid‐induced aseismic slip propagating along a stimulated fault, interactions between earthquakes. Recent theoretical and observational developments underline the important effect on seismicity migration of structural parameters, like fault criticality, or injection parameters, like flow rate or pressurization rate. Here, we analyze two well‐studied injection‐induced seismic sequences at the Soultz‐sous‐Fôret and Basel geothermal sites, and find that the evolution of the seismicity front distance primarily depends on the injected fluid volume. Based on a fracture mechanics model, we develop new equations relating seismicity migration to injected fluid volume and frictional and structural properties of the fault. We find that the propagation of a fluid‐induced aseismic slip front along the stimulated fault, triggering seismicity, explains well the observations made on the two sequences. This model allows us to constrain parameters describing the seismicity front evolution and explains the diversity of migration patterns observed in injection‐induced and natural earthquake swarms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".