Fluid-induced aseismic slip may explain the non-self-similar source scaling of the induced earthquake sequence near the Dallas-Fort Worth Airport, Texas
Bibliographic record
Abstract
Numerous studies have reported the occurrence of aseismic slips or slow slip events along faults induced by fluid injection. However, the underlying physical mechanism and its impact on induced seismicity remain unclear. In this study, we develop a numerical model that incorporates rate-and-state friction fault and fluid injection to simulate the coupled processes of pore pressure diffusion, aseismic slip, and dynamic rupture. We establish a field-scale model to emulate the induced seismicity near the Dallas-Fort Worth Airport, Texas, where events with lower stress drops have been observed. Our numerical calculations reveal that the diffusion of fluid pressure induces aseismic slips and advances or delays seismic ruptures. Furthermore, the stress drops associated with aseismic slips indicate lower values (< 1 MPa), which may explain the observed variation in stress drops near the Airport. Simulations encompassing diverse injection operations and fault frictional parameters show that the interplay between the amount of pore pressure perturbations and stress states during the interseismic period influences the initiation, quantity, recurrence intervals, and source parameters of aseismic slips. However, the scaling relationship of moment (M0) with ruptured domain (r0) for all simulated events follows an unusual trend, M0∝r04.3, similar to M0∝r04.7 observed in the Airport sequence. Based on the consistent scaling, we hypothesize that the lower stress drop events in the Airport may be less dynamic ruptures, similar to aseismic slips as illustrated in our simulations.
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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".