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 (Δp) 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 Δpand 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 Δp.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".