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
Abstract Numerous studies have reported the occurrence of aseismic slip 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 fluid injection on a fault governed by rate‐and‐state friction to simulate the coupled processes of pore‐pressure diffusion, aseismic slip, and dynamic rupture. We establish a field‐scale model to emulate the source characteristics of induced seismicity near the Dallas‐Fort Worth Airport (DFWA), Texas, where events with lower‐stress drops have been observed. Our numerical calculations reveal that the diffusion of fluid pressure modifies fault criticality and induces aseismic slip with lower stress drop values (<1 MPa), which further influence the timing and source properties of subsequent seismic ruptures. We observe that the level of pore‐pressure perturbation exhibits a positive correlation with aseismic‐stress drops but a reversed trend with seismic‐stress drops. Simulations encompassing diverse injection operations and fault frictional parameters generate a wide spectrum of slip modes, with the scaling relationship of moment ( M 0 ) with ruptured radius ( r 0 ) following an unusual trend, , similar to observed in the DFWA sequence. Based on the consistent scaling, we hypothesize that the lower‐stress‐drop events in the DFWA may imply less dynamic ruptures in the transition from aseismic to seismic slip, located in the middle of the broad slip spectrum, as illustrated in our simulations.
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 | 0.000 | 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".