Ground-Motion Analysis of Hydraulic-Fracturing Induced Seismicity at Close Epicentral Distance
Bibliographic record
Abstract
ABSTRACT The application of seismic hazard analysis methods developed for natural earthquakes can provide an effective framework for managing risks of induced seismicity (IS) sequences, particularly for assessment of potential risk to nearby infrastructure. Among various factors, the reliability of these methods depends on the accuracy of the ground-motion prediction equation (GMPE)—especially at close epicentral distances where ground motions are expected to be highest. In addition, potential impacts on local infrastructure can be assessed based on ground-motion-derived intensity values, which provide a basis for some traffic-light protocols that are used for managing fluid IS. GMPEs in many areas of the world, however, are poorly calibrated at close epicentral distances, because the availability of near-source seismograph stations is generally very sparse. This study investigates ground motions generated by an IS sequence, up to local magnitude (ML) 3.77 that occurred during a hydraulic-fracturing stimulation in the Duvernay shale play in central Alberta, western Canada. The sequence was monitored using a near-surface array that was located directly above the hydraulically fractured horizontal wells, providing accurate ground-motion measurements at hypocentral distances <10 km. The local array consisted of a combination of geophones cemented in shallow wellbores to depth of ∼27 m, shallow buried broadband seismometers, and a strong-motion accelerometer. This configuration enabled direct measurement of near-surface seismic velocities in the top 27 m, which provided robust VS30 data used to correct observed ground motions for local site-amplification effects. Our data set extends previous analyses in this region by providing new measurements very close to the epicenters of moderate earthquakes and shows that a recently developed GMPE provides accurate near-source ground-motion estimates.
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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 | 0.001 | 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".