Discovery of an Active Forearc Fault in an Urban Region: Holocene Rupture on the XEOLXELEK‐Elk Lake Fault, Victoria, British Columbia, Canada
Bibliographic record
Abstract
Abstract Subduction forearcs are subject to seismic hazard from upper plate faults that are often invisible to instrumental monitoring networks. Identifying active faults in forearcs therefore requires integration of geomorphic, geologic, and paleoseismic data. We demonstrate the utility of a combined approach in a densely populated region of Vancouver Island, Canada, by combining remote sensing, historical imagery, field investigations, and shallow geophysical surveys to identify a previously unrecognized active fault, the XEOLXELEK‐Elk Lake fault, in the northern Cascadia forearc, ∼10 km north of the city of Victoria. Lidar‐derived digital terrain models and historical air photos show a ∼2.5‐m‐high scarp along the surface of a Quaternary drumlinoid ridge. Paleoseismic trenching and electrical resistivity tomography surveys across the scarp reveal a single reverse‐slip earthquake produced a fault‐propagation fold above a blind southwest‐dipping fault. Five geologically plausible chronological models of radiocarbon dated charcoal constrain the likely earthquake age to between 4.7 and 2.3 ka. Fault‐propagation fold modeling indicates ∼3.2 m of reverse slip on a blind, 50° southwest‐dipping fault can reproduce the observed deformation. Fault scaling relations suggest a M 6.1–7.6 earthquake with a 13 to 73‐km‐long surface rupture and 2.3–3.2 m of dip slip may be responsible for the deformation observed in the paleoseismic trench. An earthquake near this magnitude in Greater Victoria could result in major damage, and our results highlight the importance of augmenting instrumental monitoring networks with remote sensing and field studies to identify and characterize active faults in similarily challenging environments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".