Crustal structure, seasonal and directional variations of ambient seismic noise in SE Canada and the NE USA
Bibliographic record
Abstract
Long-duration stacks of ambient seismic noise cross-correlation can be used to generate high-resolution images of the lithosphere. In this study, we investigate the crustal structure beneath southeastern Canada and the northeastern USA, using an ambient noise tomography technique. Our study area covers the Phanerozoic northern Appalachians and the Proterozoic eastern Grenville Province, recording a complex tectonic history since ~1 Ga. Our datasets include continuous records of vertical component time series, recorded by 69 stations belonging to 7 seismograph networks over a more than two-year period. The ambient seismic noise directionality and seasonality variations of our datasets are analyzed in detail, and possible noise source locations are proposed in the Atlantic and Pacific oceans. Our analysis suggests strong variations of dominant seismic noise sources at both Primary (11-20 s) and Secondary (5-10 s) bands in various months, with different observed patterns at these passband periods. Our tomographic models indicate complex and strong variations of Rayleigh wave phase velocities across the study area, providing us evidence to discuss tectonic implications. The resulting Rayleigh wave phase velocity maps suggest generally slower velocities beneath the Appalachians than the Grenville province. A sharp velocity contrast is observed across the Grenville Province-Appalachian domain boundary at periods sensitive to the lower crust, suggesting a step-like geometry of the Moho interface beneath this area.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".