How Induced Earthquakes Respond to Pre‐Existing Fractures and Hydraulic Fracturing Operations? A Case Study in South China
Bibliographic record
Abstract
Abstract Hydraulic fracturing in shale gas production can induce felt earthquakes, making it crucial to understand and mitigate induced earthquakes. The Cen'gong shale gas block in South China offers extensive data—3D seismic, geological structure, microseismic data, and detailed stimulation operations—allowing a comprehensive investigation into induced earthquakes by hydraulic fracturing. Using a dense temporary seismic array and deep‐learning workflows, we build a high precision earthquake catalog and determine their focal mechanisms. Pre‐existing fractures are identified through the Ant Tracking attribute derived from the 3D seismic data. We analyze the distribution, frequency, magnitude, and focal mechanisms of induced earthquakes, compare them spatially with the distribution of the pre‐existing fractures, and track their temporal changes during and after hydraulic fracturing. Most induced earthquakes occurred along pre‐existing fractures, exhibiting relatively larger magnitudes and persistent trailing seismicity. The number of trailing seismicity is proportional to the response time of stimulation earthquakes. The focal mechanism solutions suggest that the rupture mechanism of the trailing seismicity remained unchanged. By analyzing four clusters of earthquakes, we found that in two of these clusters, the induced earthquakes initiated from the far side of the fractures, then linearly migrated along the pre‐existing fractures. This directional migration pattern is explained by stress rotation along the fractures. Our analysis suggests that both pre‐existing fractures and stimulation operations significantly influence induced earthquake occurrences. Therefore, this work may enhance our understanding of pre‐existing fractures, and optimizing stimulation operations can mitigate earthquake hazards in shale gas production.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".