Nontrivial clustering of microseismicity induced by hydraulic fracturing
Bibliographic record
Abstract
Abstract For induced microseismicity associated with hydraulic fracturing, the frequency‐magnitude distribution is typically characterized by a falloff with increasing magnitude that is significantly faster than for seismicity along active fault systems. This characteristic may arise from a break in scale invariance, possibly due to mechanical layering that typifies fine‐grained sedimentary rocks in many shale gas and tight oil reservoirs. The latter would imply the presence of spatiotemporal magnitude correlations. Using three microseismic catalogs for well stimulations in widely separated locations with varying hydraulic‐fracturing methods, we show that events with similar magnitudes indeed tend to cluster in space and time. In addition, we show that the interevent time distribution can be described by a universal functional form characterized by two power laws. One exponent can be related to the presence of interevent triggering as in aftershock sequences and is a consequence of the Omori‐Utsu law. The other one is a reflection of the intrinsic spatial variation in the microseismic response rates. Taken together, these features are indicative of nontrivial spatiotemporal clustering of induced microseismicity and, hence, of direct relevance for time‐dependent seismic hazard assessment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".