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Infrasound Station Ambient Noise Estimates and Models: 2003-2006 J. Roger Bowman, Gordon Shields, and Michael S. O’Brien Science Applications International.

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Presentation on theme: "Infrasound Station Ambient Noise Estimates and Models: 2003-2006 J. Roger Bowman, Gordon Shields, and Michael S. O’Brien Science Applications International."— Presentation transcript:

1 Infrasound Station Ambient Noise Estimates and Models: J. Roger Bowman, Gordon Shields, and Michael S. O’Brien Science Applications International Corporation Presented at the Infrasound Technology Workshop Tokyo, Japan November 13-16, 2007 Approved for public release; distribution unlimited DISCLAIMER “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 2 Introduction  Objectives  Ambient infrasound noise Observations Noise models Station ranking  Correlation with station environment  Applications  Conclusions

3 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

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

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

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

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

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

9 9  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) Noise Spectrograms 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

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

11 11 More Spaghetti Microbarom peak No microbaroms Quietest site?

12 12 And Some Udon Noodles No microbaroms Surf Snow cover or pipe arrays Calibration off by a factor of 4 Not surf!! Chaparral 2

13 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 5 th /95 th percentiles Best/worst performance Infrasound Low Noise Model

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

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

16 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

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

18 18 Correlation of Noise and Installation Date  Date station put in IDC operations 1  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

19 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]

20 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

21 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


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