Directional and seasonal variations of seismic ambient noise in southeastern Canada and the NE USA
Bibliographic record
Abstract
SUMMARY Ambient seismic noise is mainly generated in oceans through the interactions between the atmosphere, ocean waves and the solid Earth. Study areas located near the edges of continents are thus subject to receiving an inhomogeneous noise field that could cause bias in ambient noise wave attenuation measurements and tomography studies. Ambient seismic noise characteristics across SE Canada and the NE USA are studied in detail at a regional scale for the first time, due to the availability of over 2 yr of data (2013–2015) recorded at 69 broad-band seismographs. This large, dense data set allowed us to use a back-projection technique to investigate both the azimuthal and temporal variations of the ambient noise. This method is based on a statistical analysis of signal-to-noise ratios (SNRs) of the waveforms in the calculated empirical Green’s functions for pairs of stations. We propose a new method of analysing the SNR by modifying the already existing concept of fan diagrams to include both causal and acausal components of the noise cross-correlograms in the analysis. We investigate directional and seasonal variations of the recorded noise data across the study area at the three main passbands of the seismic noise spectrum including the secondary microseisms (SM; 3–10 s), the primary microseisms (PM; 10–30 s), and the seismic hum (Hum; 30–300 s). We observe that the strongest and weakest signals are received at the SM and Hum bands, respectively. Considering the results of this study along with those from previous studies, we conclude that the strongest seismic noise arrivals at the three passbands investigated in this study (i.e. SM, PM and Hum) are generated at different locations in the Atlantic, Pacific and Arctic oceans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".