Earthquake Iso‐Nuisance and Iso‐Damage Mapping for Alberta: Applications for Choosing Magnitude Thresholds to Manage Induced Seismicity
Bibliographic record
Abstract
Abstract We generate earthquake iso‐nuisance and iso‐damage maps for Alberta. These maps show the spatial distribution of earthquake magnitude required to reach a specific level of nuisance and damage, considering human exposure and surficial geological conditions. We rely on population distribution for the human exposure factor while utilizing Vs30 derived from surficial geological modeling to approximate site amplification effects. By including the trailing seismicity factor, the iso‐nuisance and iso‐damage maps provide the base for the Magnitude Threshold for Acceptable Seismicity maps, which can set a guideline for the upper magnitude boundary, or largest magnitude event permissible, related to industrial activities causing seismicity. The trailing seismicity factor refers to the subsequent seismicity after a substantial change or end of the seismogenic operations; for instance, the cessation of seismogenic hydraulic fracturing activities under a traffic light protocol after a magnitude threshold event (red‐light event). Considering variations in the trailing seismicity factor, we derive different Magnitude Threshold for Acceptable Seismicity maps for various injection‐induced activities, including hydraulic fracturing and fluid disposal activities. Extended versions of the Magnitude Threshold for Acceptable Seismicity maps could allow for safety factors pertinent to critical infrastructure in a particular area, incorporating other factors beyond the population distribution and warranting a different tolerance level. These maps help to define the magnitude threshold from induced seismicity, maintaining the same tolerance levels throughout a region. Thus, they can be highly beneficial in managing current and future cases of induced seismicity related to the energy sector.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".