Evolution of Short‐Term Seismic Hazard in Alberta, Canada, From Induced and Natural Earthquakes: 2011–2020
Bibliographic record
Abstract
Abstract We generate short‐term seismic hazard maps for the province of Alberta, Canada, from 2011 through 2020. First, we describe the required adaptations to probabilistic seismic hazard analysis to generate short‐term seismic hazard maps, following the Monte Carlo simulation approach. Second, we identify the natural and induced seismic source areas in Alberta and estimate their earthquake recurrence parameters revealing considerable spatio‐temporal variations in the ‐ and ‐values in the different seismic clusters in the province. Areas with the highest short‐term seismic hazard during the last decade in Alberta are related to cases of induced seismicity, including hydraulic fracturing activities in the Duvernay Fm., near Fox Creek, and waste‐water disposal activities near the Musreau Lake. These maps provide a valuable tool to quantify the short‐term evolution in the seismic hazard, which is particularly important considering past and emerging cases of induced seismicity related to the energy sector in Alberta. Furthermore, using parameters from the previous year, we make a seismic hazard forecast for the year 2021. Our analysis provides a baseline of expected short‐term seismic hazard, with inheren uncertainty due to the assumption of unchanged recurrence parameters from the previous year; yet our study reveals pertinent seismicity patterns in line with changing human activities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".