Analysis of Very Long-Period Noise at Flexible-Array Stations in the North-American Midcontinent
Bibliographic record
Abstract
The Superior Province Rifting Earthscope Experiment (SPREE) deployed seismic stations in 2011-2013 throughout Wisconsin, Minnesota, and Ontario. To protect equipment from groundwater damage, SPREE stations were buried at unusually shallow depths, increasing the power of long period noise and facilitating an investigation into the regional effects of atmospheric tides and soil properties (Wolin et al., 2015). Here we utilize the SPREE array to study the effects of solid-earth tides and meteorological conditions, on very long-period seismic noise in the U.S. midcontinent. Continuous seismic data was collected from SPREE and Transportable Array (TA) stations located in Wisconsin and Minnesota (WIMN) between July 2011 and September 2013. This data was “cleaned”, filtered, and averaged to produce a monthly representation of the very-long period signals recorded by the SPREE stations. The signals showed diurnal (24 hr) and semidiurnal (12 hr) periodicities, whose magnitudes and dominance vary seasonally. Using cross correlations, we compare our very-long period observations with theoretical solid-earth tides (Milbert, 2018) as well as meteorological components in the WIMN region. Meteorological data, specifically temperature and pressure, was obtained from the National Oceanic and Atmospheric Administration’s (NOAA) National Center for Environmental Information (NCEI). Solid-earth tides result from the gravitational pull of the moon and sun, and have previously been documented in seismic data (e.g. Pillet et al.,1994; Lambotte et al., 2005). We observe a distinct correlation between theoretical solid-earth tides and very-long period signals in seismic data from SPREE and TA stations in the WIMN region, where one frequency component is correlated while the other appears delayed. In addition, we observe a remarkable seasonal change in SPREE recordings of these signals, but not in TA recordings. We will report our findings from testing the hypothesis that the observed very-long period signals in SPREE data are a combination of both tidal and thermal effects and that these cumulative effects are the result of the unusual burial depth of SPREE stations.
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 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 | 0.000 |
| 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 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".