Long-Term Ambient Noise Statistics in the Gulf of Mexico Mark A. Snyder & Peter A. Orlin Naval Oceanographic Office Stennis Space Center, MS Anthony I. Eller Science Applications International Corporation

Outline Monthly Trends 14-Month Statistics Variability Time Scales Coherence (Frequency, Spatial) Comparison to NDBC* Weather Data Best-Fit Probability Density Functions Summary * National Data Buoy Center

EARS* Data Bottom-moored omni-directional hydrophone Bandwidth of 10 Hz Hz 14 months of data (Apr May 2005) Water depth ~ 3200 meters Hydrophone depth ~ 2935 meters Vicinity 27.5 N, 86.1 W (about 159 nm south of Panama City, FL and 196 nm west of Tampa, FL) * Environmental Acoustic Recording System

EARS* Data Acoustic Release Floats EARS Data Logger Bottom-moored omni-directional hydrophone Bandwidth of 10 Hz Hz 14 months of data (Apr May 2005) Water depth ~ 3200 meters Hydrophone depth ~ 2935 meters Vicinity 27.5 N, 86.1 W (about 159 nm south of Panama City, FL and 196 nm west of Tampa, FL) * Environmental Acoustic Recording System

Location of EARS and NDBC* Weather Buoys NDBC EARS 103 nm 89 nm 3200 m 55 m NDBC * National Data Buoy Center

Monthly Trends

Monthly Statistics* Mean Median Standard deviation Skewness Kurtosis Coherence time** * For 8 third-octave bands. ** Time for autocorrelation to decay to e -1 of its zero-lag value.

1 year cycle Hurricanes

14-Month Statistics

Low frequency band Positive skewness Chi – Square PDF Apr04 May05

Apr04 May05 High frequency band Negative skewness Hurricanes Winter Storms

1 st order Gauss-Markov process is characterized by an exponentially-decaying autocorrelation. Coherence time = 2.97 hours

400 Hz

Variability Time Scales

Distribution of Variance* Shows how the energy associated with variability is spread over long and short time scales Each vertical bar represents the variance contribution in a 1/10-decade freq band Sum of all vertical bars = total variance At 50 Hz, most of the variability is in time scales near 10 hours At 950 Hz, most of the variability is in time scales near 100 hours * Power spectrum of each 14-month time series, plotted versus period.

Power spectrum of 14-month time series shows how the energy associated with variability is spread over long and short time scales. Each vertical bar = variance in each 1/10-decade* freq band. Sum of all vertical bars = total variance. Low frequency band Most of the variability is in time scales near 10 hours Red curve is plot of 1 st order Gauss-Markov process 4 days6 weeks1 year * 1/10-decade ≈ 1/3-octave

4 days6 weeks1 year High frequency band Most of the variability is in time scales near 100 hours

Frequency Coherence

2 octaves to left and right of center frequency have correlation coefficient ≥ 0.5

Bandwidth of High Correlation Coefficient

Spatial Coherence

A1 A3 A km 2.56 km Water depth = 3200 m at all 3 sites Hydrophone depth = 2935 m at all 3 sites 10 month comparison

100 Hz - more affected by local noise sources 1000 Hz – wind is correlated over large distances 100 Hz - more affected by local noise sources 1000 Hz – wind is correlated over large distances

Comparison to NDBC Weather Data

Ambient Noise in the Ocean (Wenz Curves)

Measured and Predicted Noise Wind speed values are followed by estimated Beaufort Wind Force in ( ). Predicted and measured noise computed at 800 Hz 1/3-octave band.

Measured and Predicted Noise Wind speed values are followed by estimated Beaufort Wind Force in ( ). Predicted and measured noise computed at 800 Hz 1/3-octave band.

14-month avg wind speed = 11.3 knots Avg significant wave height = 1.06 m Avg Beaufort Wind Force = 3.5 Moderate to heavy shipping Shipping level 6-7 on scale of month avg wind speed = 11.3 knots Avg significant wave height = 1.06 m Avg Beaufort Wind Force = 3.5 Moderate to heavy shipping (Shipping level = 6-7 on scale of 1-9, with 1 = light, 9 = very heavy)

Best-Fit Density Functions (14 Months) σ R = Rayleigh parameter. n = degrees of freedom.

Summary Highest monthly noise levels at Hz |Mean - median| < 1.5 dB (monthly values) Noise levels at higher freqs ( Hz) peaked during extremely windy months (summer hurricanes and winter storms) Standard deviation was minimized in Hz region but increased at higher freqs, especially during periods of high wind variability (hurricanes)

14-Month Summary Ambient noise at low frequencies (25 – 400 Hz) Mean > median > mode (2 – 3 dB spread) All 3 values close and predicted by moderate to heavy shipping. Location of all 3 caused positive skewness (skewed towards peaks). Ambient noise at high frequencies (630 – 950 Hz) Mode > median > mean (2 – 3 dB spread) All 3 values close and predicted by avg BWF = 3.5 (11.3 knots avg wind). Location of all 3 caused negative skewness (skewed towards troughs).

14-Month Summary Coherence time was low (2 – 4 hours) in shipping bands (25 – 400 Hz) Coherence time was high (14 – 21 hours) in weather bands (630 – 950 Hz) Monthly coherence time was highest during extreme wind conditions

14-Month Summary The best fit for the 25 Hz and 200 Hz bands were Rayleigh PDFs (first 3 moments matched) The best fit for the 50 Hz and 100 Hz bands were Chi-Square PDFs (first 3 moments matched) The monthly and 14-month average noise levels agreed with predictions based on moderate to heavy shipping and the average weather as determined by the nearby NDBC buoys

14-Month Summary Temporal variability occurred over 3 time scales:  hours (shipping-related)  hours ( days, weather-related)  months (1 year cycle) The 25 Hz time series had a strong 8-hour component (sinusoidal autocorrelation; not shipping or weather) The 50, 100 and 200 Hz frequency bands were fit by a 1st order Gauss-Markov process (well characterized by 3 parameters: mean, variance and coherence time) More complicated structure in other bands

Avg BWF = 2.5 Mean = dB σ = 5.78 dB Range = dB Skewness = 0.45 C.T. = 1.74 hours Avg BWF = 4 Mean = dB σ = 4.67 dB Range = dB Skewness = C.T. = hours

Data Processing 2048 Point FFT 10 Minute Avg Power Spectra Separate Data Into 14 Months Bandpass Each Month’s Data Over Eight 1/3-Octave Bands Compute Monthly Statistics Over Each Frequency Band Raw Acoustic Time Series Data Average 732 (0.82 seconds each) periodograms. Sampled at 2.5 kHz. Remove disk spin and clips. Compute the average power in each band every 10 minutes. Δf = 1.22 Hz. F C = 25, 50, 100, 200, 400, 630, 800, 950 Hz

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