Mapping Seismic Tonal Noise in the Contiguous United States
Bibliographic record
Abstract
Abstract A subset of seismic signals generated in industrial environments displays spectral peaks organized in sequences of fundamental frequencies with multiple overtones, which we refer to as tonal noise (TN). Using one year of data from each of 1732 stations in the USArray Transportable Array, we detected around 1.5 million TN sequences in the contiguous United States, which corresponds (on average) to around 2.4 detections per day (869 detections per year) at each station. TN across the continent is clustered around specific regions and frequencies. The majority (>70%) of stations in the 90th percentile of total detection numbers (more than 2100 detections per year) are concentrated in the Interior Plains, Canadian Shield, and Appalachian Highlands. We found that the fundamental frequencies of all TN detections are concentrated in six spectral bands with value ranges of 0.9–0.95, 1.8–1.85, 2.5–2.55, 3.3–3.35, 5–5.05, and 5.45–5.5 Hz with around 104, 37, 46, 37, 62, and 45 thousand detections, respectively. Detections in these bands account for around 22% of all detections. We suggest that large regions with similar TN are related to noise from industrial activities driven by physiographic characteristics such as favorable winds or abundance of water (wind and hydroelectric power generators). The presence of TN and other spectrally discrete components in the seismic wavefield is a ubiquitous feature in the seismic background. This type of noise has the potential to affect subsurface imaging efforts by introducing potentially static and continuous sources of noise. The effects of TN can be especially significant as near-surface imaging studies move toward utilizing higher frequency (>1 Hz) for ambient seismic noise.
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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 | 0.002 |
| 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) | 0.000 | 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".