Shallow Lingering and Deep Transient Seismicity Related to Hydraulic Fracturing in the Changning Shale Gas Field, Sichuan Basin, China
Bibliographic record
Abstract
Abstract Characterizing seismic responses to hydraulic fracturing (HF) in shale‐gas development is crucial for seismic‐hazard assessment and mitigation‐strategy design. Although intensive HF operations have led to severe induced seismic hazards in the Changning shale gas field (CSF) in China for over a decade, the typical spatiotemporal characteristics of induced seismicity during and after HF in this region remain unclear, due to a lack of detailed fluid‐injection data. Using a 70‐day‐long dense deployment of 336 nodal‐sensors in 2019, we develop an enhanced seismicity catalog and combine it with focal mechanism solutions, fluid‐injection time series, seismic‐reflection profiles, and geomechanical models to identify the distinct shallow and deep seismicity responses to HF. The first pattern consists of deep earthquake clusters that migrate along strike‐slip faults in the limestone formation ∼1 km below the treatment depth. These clusters contain frequent earthquakes, including the largest event, and exhibit transient seismicity‐rate changes in rapid response to HF. In contrast, the second pattern consists of shallow clusters in the target shale formation that persist for over a year following HF. The shallow clusters include smaller earthquakes and exhibit thrust‐style faulting with no discernible spatial migration. Our geomechanical simulations suggest the deep fault reactivation is best explained by the combined effects of poroelastic‐stress loading and pore‐pressure increases. Stable seismicity rate and frequent casing deformation indicate post‐HF, long‐term aseismic deformation may drive the shallow seismicity. These distinct seismic responses during and after HF operations underscore the need for a spatiotemporally adaptive hazard mitigation strategy for the CSF.
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 | 0.002 | 0.001 |
| 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".