Temporal Variation in Cultural Seismic Noise and Noise Correlation Functions during COVID-19 Lockdown in Canada
Bibliographic record
Abstract
Abstract The COVID-19 pandemic of 2020 led to a widespread lockdown that restricted human activities, particularly land, air, and maritime traffic. The “quietness” on land and ocean that followed presents an opportunity to measure an unprecedented reduction in human-related seismic activities and study its effect on the short-period range of ambient noise cross-correlation functions (NCFs). We document the variations in seismic power levels and signal quality of short-period NCFs measured by four seismographs located near Canadian cities across the pandemic-defined timeline. Significant drops in seismic power levels are observed at all the locations around mid-March. These drops coincide with lockdown announcements by the various Canadian provinces where the stations are located. Mean seismic power reductions of ∼24% and ∼17% are observed near Montreal and Ottawa, respectively, in eastern Canada. Similar reductions of ∼27% and 17% are recorded in western Canada near Victoria and Sidney, respectively. None of the locations show full recovery in seismic power back to the pre-lockdown levels by the end of June, when the provinces moved into gradual reopening. The overall levels of seismic noise during lockdown are a factor of 5–10 lower at our study locations in western Canada, relative to the east. Signal quality of NCF measured in the secondary microseism frequency band for the station pair in western Canada is maximum before lockdown (late February–early March), minimum during lockdown (mid–late March), and increased to intermediate levels in the reopening phase (late May). A similar pattern is observed for the signal quality of the eastern Canada station pair, except for a jump in levels at similar periods during the lockdown phase. The signal quality of NCF within the secondary microseism band is further shown to be the lowest for the western Canada station pair during the 2020 lockdown phase, when compared with similar time windows in 2018 and 2019.
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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 | 0.002 |
| 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 | 0.001 |
| 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".