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.
| Original language | English |
|---|---|
| Pages (from-to) | 1707-1716 |
| Number of pages | 10 |
| Journal | Seismological Research Letters |
| Volume | 91 |
| Issue number | 3 |
| DOIs | |
| State | Published - May 1 2020 |
| Externally published | Yes |
Funding
Science & Technology Institute and the Office of the Chief Information Officer for initial support of this work. The authors also thank Chad Trabant, Robert Weekly, Robert Casey, and Inge Watson at the Incorporated Research Institutions for Seismology Data Management Center (IRIS-DMC) for their collaboration during the test and deployment of our analysis platform. This article has been authored by Triad National Security under Contract Number 89233218CNA000001 with the U.S. Department of Energy. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paidup, irrevocable, worldwide license to publish or reproduce the published form of this article, or allow others to do so, for the U.S. Government purposes. The authors thanks the Editor-in-Chief Allison Bent and two reviewers for providing very valuable comments and suggestions. The authors would like to thank the Los Alamos Information Science & Technology Institute and the Office of the Chief Information Officer for initial support of this work. The authors also thank Chad Trabant, Robert Weekly, Robert Casey, and Inge Watson at the Incorporated Research Institutions for Seismology Data Management Center (IRIS-DMC) for their collaboration during the test and deployment of our analysis platform. This article has been authored by Triad National Security under Contract Number 89233218CNA000001 with the U.S. Department of Energy. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paidup, irrevocable, worldwide license to publish or reproduce the published form of this article, or allow others to do so, for the U.S. Government purposes.
| Funders | Funder number |
|---|---|
| Editor-in-Chief Allison Bent | |
| IRIS-DMC | 89233218CNA000001 |
| Los Alamos Information Science & Technology Institute | |
| U.S. Government | |
| U.S. Department of Energy | |
| Office of Chief Information Officer |
