Strategies to prevent damage to critical infrastructure due to induced seismicity
Bibliographic record
Abstract
There has been a significant increase in the rate of felt earthquakes in western Alberta and eastern British Columbia, which has been associated with hydraulic fracturing and wastewater disposal. The increased rate of seismicity and the potential for localized strong ground motions from very shallow events poses an increased hazard to critical infrastructure such as major dams—particularly for older high-consequence structures. This paper overviews the factors that affect the likelihood of damaging ground motions and examines their implications for hazard assessment and mitigation. A strategy aimed at reducing the likelihood of potentially damaging ground motions to achieve probabilistic targets for critical facilities is developed, comprising elements of both mitigation and avoidance. For critical facilities, an effective strategy includes (i) an exclusion zone having a radius of ∼5 km; and (ii) a monitoring-and-response protocol to track the rate of events at the M > 2 level within 25 km, with adjustment of operational practices if required. An exclusion zone provides a deterministic safety margin to ensure the integrity of those few facilities for which failure consequences are unacceptable. Real-time monitoring tied to a response protocol can be used to control the rate of significant events and thereby limit the hazard more broadly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".