Strong directivity of ocean‐generated seismic noise
Bibliographic record
Abstract
We measure direction and amplitude of ocean‐generated continuous seismic noise in the western United States. Slowness direction of the noise is determined using array beamforming, and particle motion direction from individual three‐component stations. We find two surprising results. First, the noise is highly monodirectional at all sites, regardless of coastal distance. A single narrow generation area dominates for most of the time period, interrupted by a second well defined direction during ocean swell events. Second, we find that a storm off the Labrador coast with not unusual wave heights generates coherent noise across the entire continent. We show the causal relationship between swells arriving at different North American coastal areas and the triggered microseisms in time‐lapse movies ( Animations 1 and 2 ) of ocean swells and concurrent microseisms. Our results have a number of implications for different fields of research. A useful by‐product of our finding that microseisms are a strongly directional noise source is the possibility of using automated processing of the continuous noise as a near real‐time check on station polarity and calibration problems, which would be a simply implemented indicator for the state of health of a seismic network. Consistent monodirectional noise may have an influence on seismic azimuthal measurements such as shear wave splitting. Most importantly, our findings should be taken into account in proposed studies which will use seismic noise as a proxy for ocean wave height in investigations of interdecadal climate change.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".