Red-light thresholds for induced seismicity in the UK
Bibliographic record
Abstract
Induced earthquakes pose a serious hurdle to subsurface energy development. Concerns about induced seismicity led to terminal public opposition of hydraulic fracturing in the UK. Traffic light protocols (TLPs) are typically used to manage these risks, with the red-light designed as the last-possible stopping-point before exceeding a risk tolerance. We simulate trailing earthquake scenarios for the UK, focusing on three risk metrics: nuisance, damage, and local personal risk (LPR) – the likelihood of building collapse fatality for an individual. The severity of these risks can spatially vary (by orders-of-magnitude), depending on exposure. Estimated risks from the Preston New Road earthquakes are used to calibrate our UK earthquake risk tolerances, which we find to be comparable to Albertan (Canadian) tolerances. We find that nuisance and damage concerns supersede those from fatality and that the safest regions for Bowland Shale development would be along the east coast. A retrospective comparison of our TLP result with the Preston New Road case highlights the importance of red-light thresholds that adapt to new information. Overall, our findings provide recommendations for red-light thresholds (ML 2-2.5) and proactive management of induced seismicity – regardless of anthropogenic source.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".