Fault-Source-Based Probabilistic Seismic Hazard and Risk Analysis for Victoria, British Columbia, Canada: A Case of the Leech River Valley Fault and Devil’s Mountain Fault System
Bibliographic record
Abstract
This study develops a fault-source-based seismic hazard model for the Leech River Valley Fault (LRVF) and the Devil’s Mountain Fault (DMF) in southern Vancouver Island, British Columbia, Canada. These faults pose significant risks to the provincial capital, Victoria, due to their proximity and potentially large earthquake magnitudes. To evaluate the effects of including these faults in probabilistic seismic hazard analysis and city-wide seismic loss estimation for Victoria, a comprehensive sensitivity analysis is conducted by considering different fault rupture patterns and different earthquake magnitude models, as well as variations in their parameters. The aim is to assess the relative contributions of the LRVF-DMF system to the overall seismic hazard and risk in Victoria at different return periods. The consideration of the LRVF-DMF system as a potential seismic source increases the seismic risk assessment results by 10 to 30%, especially at the high return period levels. The sensitivity analysis results highlight the importance of determining the slip rate for the fault deformation zone and of specifying the earthquake magnitude models (e.g., characteristic versus truncated exponential models). From urban seismic risk management perspectives, these nearby faults should be considered critical earthquake scenarios.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".