Stress Drops and Directivity of Induced Earthquakes in the Western Canada Sedimentary Basin
Bibliographic record
Abstract
Abstract The Western Canada sedimentary basin (WCSB) has experienced an increase in seismicity during the last decade due primarily to hydraulic fracturing. Understanding the ground motions of these induced earthquakes is critical to characterize the increase in hazard. Stress drop is considered an important parameter in this context because it is a measure of the high‐frequency content of the shaking. We use the empirical Green’s function (EGF) method to determine S‐wave corner frequencies and stress drops of 87 earthquakes of moment magnitude (M) 2.3–4.4 in the WCSB. The EGF method is an effective technique to isolate earthquake source effects by dividing out the path and site components in the frequency domain, using a smaller collocated earthquake as an EGF. The corner frequency of the target event is determined for an assumed spectral ratio shape, from which the stress drop is computed. Assuming a fixed velocity, we find that the average stress drop for induced earthquakes in the WCSB for small‐to‐moderate events is 7.5±0.5 MPa, with a total range from 0.2 to 370 MPa. However, because of the dependence of stress drop on model conventions and constants, we consider the absolute stress‐drop value meaningful only for comparison with other results using the same underlying models. By contrast, corner frequency is a less‐ambiguous variable with which to characterize the source spectrum. The range of corner frequencies obtained in this study for events of M 4.0±0.5 is 1.1–5.8 Hz. Significant rupture directivity is observed for more than one‐third of the earthquakes, with station corner frequencies varying by about a factor of 4 with azimuth. This emphasizes the importance of having suitable station coverage to determine source parameters. We model directivity where evident using a Haskell source model and find that the rupture azimuths are primarily oriented approximately north–south throughout the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".