Improving Situational Awareness During Early Earthquake Response Using Existing Seismic Risk Models to Rapidly Estimate Damage
Bibliographic record
Abstract
In the first several hours following an earthquake, municipalities are often forced to rely upon reports from first responders, reconnaissance along disrupted roadways by emergency personnel, or wait for aerial surveillance and remote sensing. The latter is expected to take at least 12 hours, a crucial period following a major earthquake in which situational awareness can be greatly improved using existing seismic risk modelling tools. This work presents a new initiative to develop a rapid disaster modelling protocol for earthquakes in British Columbia (BC). We explore best practices and the feasibility of using immediately available seismic data in the existing OpenQuake Canada framework to model the impacts to people, the built environment, and the economy from an earthquake in near real-time. The current prototype integrates observed ground motion data from regional strong motion seismometers, like the BC Smart Infrastructure Monitoring System, with physical exposure data from Natural Resources Canada’s Human Settlement Layer to report on key metrics for early response: collapsed buildings, entrapment injuries, hospital demand surge, roadway debris which may block response, and immediate mass care needs like shelter requirements. These indicators will be ported to the British Columbia Common Operating Picture Portal, the online situational awareness and mapping platform for authoritative, collaborative and coordinated distribution of emergency management information in the province. These outputs could be made available within tens of minutes of the earthquake occurring, potentially affording emergency managers the opportunity to best direct resources to save lives and reduce suffering.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".