M. Garces, D. Fee, J. Park Infrasound Laboratory, University of Hawaii, Manoa F. Ham Florida Institute of Technology Surf Infrasound from Oahu’s North.

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Presentation transcript:

M. Garces, D. Fee, J. Park Infrasound Laboratory, University of Hawaii, Manoa F. Ham Florida 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 Shark’s Cove = Lava Bell North Swell: Log Cabins NW-W: Pipeline Big NW-W: Waimea/Pinballs

N. Shore Deployment: Winter Note bubble cloud dimensions > 2 m

N. Shore Deployment

Neural Networks at North Shore (Ham) Confusion Matrix Prediction ActualActual Pin- balls Pipe- line Shark’s cove Unk- nown Total Pin- balls 7204(3)480 Pipe- line 1646(1)980 Shark’s cove 1(2) 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 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)

f R (ω) – Transformation from Breaking Wave Height to temporal evolution of Characteristic Spatial Dimension of bubble plume. f A (ω) – Transform from Characteristic Plume Dimensions to Spectrum of Radiation Frequencies

Bubble Oscillation Model at Polihale (Park)  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.