Seismic Background Noise Levels across the Continental United States from USArray Transportable Array: The Influence of Geology and Geography
Bibliographic record
Abstract
ABSTRACT Since 2004, the most complete estimate of background noise levels across the continental United States was attained using 61 broadband seismic stations to calculate power spectral density (PSD) probability density functions. To improve seismic noise estimates across the United States, we examine vertical component seismic data from the EarthScope USArray Transportable Array seismic network that rolled across the United States and southeastern Canada between 2004 and 2015 and form a large (10 TB) PSD database from 1679 stations that contains no smoothing or binning of the spectral estimates. Including station outages, our database has a mean of 98.9% data completeness, and we present maps showing the spatial and temporal variability of seismic noise in six bands of interest between 0.2 and 75 s period. At 0.2 s period, seismic noise across the eastern United States is predominantly anthropogenically generated and may be subsequently amplified more than 20 decibels in the sandy and water-saturated sediments of the southeastern U.S. Coastal Plain and Mississippi Embayment. In these sediments, 1 s noise shows similar amplification and is generated through a variety of mechanisms across the United States including cultural activity throughout Kentucky and the southeastern Appalachian Mountains, lake waves around the Great Lakes, and ocean waves throughout New England, the Pacific Northwest, and Florida. Both 0.2 and 1 s noise levels are the lowest in the Intermountain West portion of the United States. We attribute this to a combination of installations on crystalline rocks and reduced population density. Finally, we find that sensors emplaced in sandy, water-saturated sediments observe median, diurnal variations in vertical component power at 18–75 s period, which we infer arise through local deformation driven by pressure variations. Ultimately, our results underscore that for shallow (<5 m depth) sensor installation, bedrock provides superior broadband noise performance compared to unconsolidated sediments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".