Introduction

1. Infrasound Station Ambient Noise Estimates and Models: 2003-2006J. Roger Bowman, Gordon Shields, and Michael S. O’Brien Science Applications International Corporation Presented at the Infrasound Technology WorkshopTokyo, JapanNovember 13-16, 2007Approved for public release; distribution unlimitedDISCLAIMER“The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either express or implied, of the U.S. Army Space and Missile Defense Command or the U.S. Government.”

2. Introduction • Objectives • Ambient infrasound noise • Observations • Noise models • Station ranking • Correlation with station environment • Applications • Conclusions 2

3. Objectives • Characterize infrasound noise environment of all existing infrasound stations • Provide basis for assessing station capability • Define noise models for infrasound stations • Examine relationship of noise and basic station characteristics 3

4. Comparison with Previous Studies 1. Bowman, J.R., G.E. Baker, and M. Bahavar, Ambient infrasound noise, Geophys. Res. Lett., 32 L09803, doi: 10.1029/2005GL022486, 2005. 2. Infrasound Technology Workshop, Tahiti, 2005. 4

5. Stations in Study New stations for this study Previous study All 39 stations with data available in August 2006 5

6. Calculate spectra and PSD Identify anomalies Method • 4 years • 4 times/day • 1 hour intervals • 21 3-minute samples/hour • 3,000,000 spectra • Station medians • Station 5th and 95th percentiles • Network median • Seasonal variation • Diurnal variation • 39 stations • 34 IMS • 5 non-IMS Waveform archive Calculate summary spectral statistics Define noise models Station database 6

7. All spectra Median spectra 5th, 95th percentile Global median for all stations Sample Noise Estimate: I53US • Fairbanks, Alaska • Spring • 12 PM – 1 PM outliers outliers 7

8. All spectra Median spectra 5th, 95th percentile Global median for all stations Sample Noise Estimates: I18DK 4 seasons 4 times/day Number of PSD plotted Similar plots for all 39 stations are available for review at this workshop 8

9. Noise Spectrograms • Median spectrum for each day for the interval 6 – 7 AM • Shows different character of noise at different stations • (Dark blue where no data are available) Microbaroms washed out by wind Winter peaks in microbaroms Winter peaks in microbaroms Similar plots for all 39 stations are available for review at this workshop 9

10. Comparison Among Stations: Winter 6–7 AM No microbaroms Floor of MB2000s? Microbarom peak Anti-alias filter 10

11. More Spaghetti Nomicrobaroms Microbarom peak Quietest site? 11

12. And Some Udon Noodles Nomicrobaroms Surf Not surf!! Chaparral 2 Calibration off by a factor of 4 Snow cover or pipe arrays 12

13. Infrasound Noise Models • Purpose • Evaluate individual station performance • Evaluate requirements for instrument self noise • Data used • 29 stations • 12 months per station • Network median • All stations, all, seasons, all times • “Typical” noise level • Low/high noise models • At each frequency, minimum/maximum among all stations of 5th/95th percentiles • Best/worst performance Infrasound Low Noise Model 13

14. Comparison of Noise Models Noisier stations added to network Median noise models similar I55US removed from low-noise model (possible issues with snow and ice) 14

15. Stacked Power Spectral Density (PSD) Anti-aliasing filters 39 stations 3 million PSDs No visible microbaroms Log Number of PSD Network median Microbarom peak MB2000 floor? I55 15

16. Station Capability • What makes a “good” station? • Station location relative to potential sources (network design) • Records “real” signals • Low ambient noise (siting, wind, vegetation: this study) • Appropriate instrumentation (station design) • Array aperture, inter-sensor spacing, self-noise, wind-noise reduction filters • Reliability of instrumentation and communications (O&M) • Difficult to tell if a station is “good” • Few signals of interest or surrogates • Diurnal and seasonal variations complicate comparison • Frequency-dependent noise and signal spectra 16

17. Assessing Station Performance Ordered by time with noise <25th percentile Time a station is ranked in three global-noise percentiles 17

18. Correlation of Noise and Installation Date • Date station put in IDC operations1 • Trend of increasing noise with time(at 0.2 Hz and 1 Hz) • Less accessible (and noisier) stations installed after easier ones 1. From PTS monthly report: Station of Station Connections and Availability of Data Station Installation Date Station Installation Date 18

19. Correlation of Noise and Distance to Ocean • Mean noise decreases with distance from nearest ocean(at 0.2 Hz and 1 Hz) Distance to Nearest Ocean [km] Distance to Nearest Ocean [km] 19

20. Correlation of Noise and Land Cover • Land cover categories • None • Herbaceous and sparse shrub • Shrub and sparse trees • Dense trees • Noise decreases with more dense vegetation (at 0.2 Hz and 1 Hz) Amount of Ground Cover Amount of Ground Cover 20

21. Conclusions • Ambient noise is highly variable by station, season and time of day • Infrasound noise models can be used to assess potential station capability • Simple metric can be used to objectively compare station noise • Noise at IMS stations increases with installation date • Noise at IMS stations decreases with distance from oceans and with density of vegetation 21