Fluid‐Earthquake and Earthquake‐Earthquake Interactions in Southern Kansas, USA
Bibliographic record
Abstract
Abstract An increase in injection activity associated with energy production in southern Kansas starting in 2013 has been linked to the occurrence of more than 130,000 earthquakes (M −1.5 to 4.9) between 2014 and 2017. Studies suggest that the dramatic increase in seismicity rate is related to wastewater injection into the highly permeable Arbuckle formation. Most of the seismicity is located in the underlying crystalline basement, for which hydrological properties and specific fault geometries are unknown. Additionally, some earthquake clusters occurred relatively far (tens of kilometers) from the main injection wells. Therefore, the effect of pore pressure diffusion may be insufficient to explain the relation between the volume of injected fluids and the spatiotemporal evolution of seismicity. Combining physical models (static stress and poroelasticity) and a statistical cluster analysis applied to a high‐resolution relocated catalog, we analyze the evolution of seismicity in southern Kansas. We find that pore pressure changes (Δ p ) and Coulomb stress changes (ΔCFS) due to fluid diffusion smaller than 0.1 MPa are enough to initiate seismic sequences, which then evolve depending on their distance from the major injection wells. However, we find that earthquake sequences have different seismogenic responses to Δ p and ΔCFS in terms of triggering threshold. In regions located close to disposal wells (Harper area) our cluster analysis suggests that both earthquake interactions and fluid diffusion control the evolution of seismicity. On the other hand, at greater distances (Milan area), where clustering behavior suggests greater earthquake interactions, we find that coseismic ΔCFS are larger than Δ p .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".