Seismic velocity model of the crust in the northern Canadian Cordillera from Rayleigh wave dispersion data
Bibliographic record
Abstract
Models of the seismic velocity structure of the crust in the seismically active northern Canadian Cordillera remain poorly constrained, despite their importance in the accurate location and characterization of regional earthquakes. On 29 August 2014, a moderate earthquake with magnitude 5.0, which generated high-quality Rayleigh wave data, occurred in the Northwest Territories, Canada, ∼100 km to the east of the Cordilleran Deformation Front. We carefully selected 23 seismic stations that recorded the Rayleigh waves and divided them into 13 groups according to the azimuth angle between the earthquake and the stations; these groups mostly sample the Cordillera. In each group, we measured Rayleigh wave group velocity dispersion, which we inverted for one-dimensional shear-wave velocity models of the crust. We thus obtained 13 models that consistently show low seismic velocities with respect to reference models, with a slow upper and lower crust surrounding a relatively fast mid crustal layer. The average of the 13 models is consistent with receiver function data in the central portion of the Cordillera. Finally, we compared earthquake locations determined by the Geological Survey of Canada using a simple homogenous crust over a mantle half space with those estimated using the new crustal velocity model, and show that estimates can differ by as much as 10 km.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".