Infrasound Station Ambient Noise Estimates and Models:

2003-2006J. Roger Bowman, Gordon Shields, and

Michael S. O’Brien

Science Applications International Corporation

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, 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.”

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IntroductionIntroduction

Objectives Ambient infrasound noise

• Observations

• Noise models

• Station ranking

Correlation with station environment Applications Conclusions

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ObjectivesObjectives

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

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Comparison with Previous StudiesComparison 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.

12/041 12/052 11/07

Stations analyzed 21 28 39

Stations in models 11 15 29

Spectra calculated 600,000 1,500,000 3,000,000

Months analyzed 12 24 44

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Stations in StudyStations in Study

All 39 stations with data available in August 2006

New stations for this study

Previous study

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MethodMethod

Waveform archive

Station database

Calculatespectra 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

Calculatesummary spectral statistics

Identify anomalies

Station medians Station 5th and 95th

percentiles Network median Seasonal variation Diurnal variation

Define noise models

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Sample Noise Estimate: I53USSample Noise Estimate: I53US

All spectra

Median spectra

5th, 95th percentile

Global median for all stations

outliers

outliers • Fairbanks, Alaska

• Spring

• 12 PM – 1 PM

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Sample Noise Estimates: I18DKSample Noise Estimates: I18DK4 seasons

4 times/day

All spectra

Median spectra

5th, 95th percentile

Global median for all stations

Similar plots for all 39 stations are Similar plots for all 39 stations are available for review at this workshopavailable for review at this workshop

Number of PSD plottedNumber of PSD plotted

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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 SpectrogramsNoise Spectrograms

Microbaroms washed out by wind

Winter peaks inmicrobaroms

Winter peaks inmicrobaroms

Similar plots for all 39 stations are Similar plots for all 39 stations are available for review at this workshopavailable for review at this workshop

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Comparison Among Stations: Winter 6–7 AMComparison Among Stations: Winter 6–7 AM

Microbarompeak

No microbaroms

Floor of MB2000s?

Anti-alias filter

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More SpaghettiMore Spaghetti

Microbarompeak

No microbaroms

Quietest site?

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And Some Udon NoodlesAnd Some Udon Noodles

No microbaroms

Surf

Snow cover or

pipe arrays

Calibration off by a factor of 4

Not surf!!Chaparral 2

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Infrasound Noise ModelsInfrasound 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

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Comparison of Noise ModelsComparison of Noise Models

I55US removed from low-noise model (possible issues with snow and ice))

Noisier stations added to network

Median noise models similar

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Stacked Power Spectral Density (PSD)Stacked Power Spectral Density (PSD)

39 stations

3 million PSDs

Log N

umber of P

SD

Microbarompeak

No visiblemicrobaroms

Anti-aliasingfilters

MB2000 floor?

I55

Network median

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Station CapabilityStation 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

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Assessing Station PerformanceAssessing Station Performance

Time a station is ranked in three global-noise percentiles

Ordered by time with noise <25th percentile

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Correlation of Noise and Installation DateCorrelation 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

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Correlation of Noise and Distance to OceanCorrelation 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]

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Correlation of Noise and Land CoverCorrelation 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

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ConclusionsConclusions

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