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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 Corporation.

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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.”

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

2

objectives
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

comparison with previous studies
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

stations in study
Stations in Study

New stations for this study

Previous study

All 39 stations with data available in August 2006

5

method

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

sample noise estimate i53us

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

sample noise estimates i18dk

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

noise spectrograms
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

comparison among stations winter 6 7 am
Comparison Among Stations: Winter 6–7 AM

No microbaroms

Floor of

MB2000s?

Microbarom

peak

Anti-alias filter

10

more spaghetti
More Spaghetti

Nomicrobaroms

Microbarom

peak

Quietest site?

11

and some udon noodles
And Some Udon Noodles

Nomicrobaroms

Surf

Not surf!!

Chaparral 2

Calibration off by a factor of 4

Snow cover or

pipe arrays

12

infrasound noise models
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

comparison of noise models
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

stacked power spectral density psd
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

station capability
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

assessing station performance
Assessing Station Performance

Ordered by time with noise <25th percentile

Time a station is ranked in three global-noise percentiles

17

correlation of noise and installation date
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

correlation of noise and distance to ocean
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

correlation of noise and land cover
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

conclusions
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