Surf Infrasound from Oahu’s North Shore: Real-time Monitoring of the Seven Mile Miracle
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Transcript of Surf Infrasound from Oahu’s North Shore: Real-time Monitoring of the Seven Mile Miracle
M. Garces, D. Fee, J. ParkInfrasound Laboratory, University of Hawaii, Manoa
F. HamFlorida Institute of Technology
Surf Infrasound from Oahu’s North Shore: Real-time Monitoring of the Seven Mile Miracle
Background: HI deployments
BACKGROUND
Moorea Deployment: Temae Reef
• Wave gauge
• Infrasound array
• 3C broadband seismometer
• Video camera (GPS time on frames)
Previous Conclusions• Possible to acoustically track one wave breaking - Progressive
Wavefront Tracking• Sound appears to scale with breaking ocean wave intensity• Possible to determine swell direction and period• Source process identification
– Fluid impact– Bubble cloud oscillation– Gas ejection
• Want to apply and test methods in near real-time environment
N. Shore Deployment, Oahu• Expressed interest from NOAA/NWS Honolulu
• Deployed array ~2 km from shoreline (Shark’s Cove).
• Test real-time operational system to monitor broad coastal sections of North Shore
• Correlate observations with Waimea directional buoy and other available data
• Targeted GPS-timed video/IR camera measurements
N. Shore Deployment: Winter 2006-07
• Shark’s Cove = Lava Bell
• North Swell: Log Cabins
• NW-W: Pipeline
• Big NW-W: Waimea/Pinballs
N. Shore Deployment: Winter 2006-07• Note bubble cloud dimensions > 2 m
N. Shore Deployment
N. Shore Deployment
Neural Networks at North Shore (Ham)Confusion Matrix
Prediction
Actual
Pin-balls
Pipe-line
Shark’scove
Unk-nown
Total
Pin-balls
72 0 4(3) 4 80
Pipe-line
1 64 6(1) 9 80
Shark’scove
1(2) 0 114 5 120
F.M. Ham, R. Acharyya, Y-C. Lee, M. Garces, D. Fee, C. Whitten, and E. Rivera, "Classification of Infrasound Surf Events Using Parallel Neural Network Banks," In the Proceedings of the International Joint Conference of Neural Networks, August 12-17, 2007, Orlando, FL, pp. 720-725.
Diagonal numbers indicate the number of correctly classified signals. The off diagonal elements are associated with the number of misclassifications.
Bubble Oscillation Model at Polihale (Park)
Bubble Oscillation Model at Polihale (Park)
0.0 0.5 1.0 1.5 2.0P lum e R adius (m )
1
10
100
f (H
z)
1
10
100
Void Fraction
0.3
0.4
0.5
fc - Sem icylinder
fs - Spherica l
D epth 1m
0.0 0.2 0.4 0.6 0.8 1.0t / T
1.0
1.2
1.4
1.6
1.8
RB
t/RB
0 = 1
/2 .
(A*)
1/2
1.0
1.2
1.4
1.6
1.8
fR(ω) – Transformation from Breaking Wave Height to temporal evolution of Characteristic
Spatial Dimension of bubble plume.
fA(ω) – Transform from Characteristic Plume Dimensions to Spectrum of Radiation
Frequencies
Bubble Oscillation Model at Polihale (Park)
0
5
10
15
20
25
30
Am
plitu
de (
dB)
10 knotsSW H = 1.7 m = 0.099 H z
0
5
10
15
20
25
Am
plitu
de (
dB)
30 knotsSW H = 4.5 m = 0.069 H z
20 knotsSW H = 3.1 m = 0.079 H z
40 knotsSW H = 5.9 m = 0.062 H z
a) b)
c) d)
0 4 8 12 16 20Frequency (H z)
0 4 8 12 16 20Frequency (H z)
Using open ocean wave spectrum, compared modeled (blue) and observed (red) infrasound spectra from Polihale, Kauai.
Even with gross representation of geophysical parameters, the model seems to capture the essential characteristics of surf infrasound which include spectral shape, migration of dominant infrasonic energy to lower frequencies as ocean wave energy increases, and a broadening of the infrasonic energy distribution across the main lobe.
Concluding Remarks
Operational acoustic surf monitoring systems are
feasible and may be useful for nowcasting and shoreline hazard
assessment.
Ongoing progress on modeling of surf
infrasound signals would permit extraction of
oceanographic information from the
acoustic data.