1. Changes in the Performance of the IMS Infrasound Network due to Seasonal Propagation Effects David Norris and Robert Gibson BBN Technologies 1300 N. 17 th Street Arlington, VA 22209 Infrasound Technology Workshop La Jolla, California 27-30 Oct 2003

2. Outline Network Performance Issues Simulation Approach Results (multimedia!) Conclusions

3. Network Performance Factors Network coverage Array performance and signal-to-noise ratio Propagation effects and uncertainties

4. Network Coverage Lack of azimuthal coverage can lead to elongated error ellipses Example: Pacific bolide 23 Apr 01: IS57 NVIAR DLIAR IS59 IS10 IS26 Source

5. Signal-to-Noise Ratio Some stations are inherently noisier than others –Full exposure vs. cover (e.g. tree canopy) –Island vs. mainland –Regional wind conditions (e.g. Windless Bight vs. Palmer) Signal gain –Nominal array beamforming gain: 10log(N) –Nominal Bandwidth gain: 5log(W) To improve SNR –Increase number of sensors –Improve wind filter –Advanced signal processing

6. Propagation factors Stratospheric arrival –Shorter path –Less absorption –Duct presence depends on stratospheric winds With wind Counter wind Thermospheric arrival –Longer path –More absorption –Duct always present Stratospheric duct thermospheric duct

7. Propagation Parameterizations Signal Strength –Empirical equations that account for effect of stratospheric winds –Received pressure (P) function of range (R), yield (W) and winds at 50 km (V s ) –38 dB difference between 50 m/s upwind and downwind propagation Mutschlecner, J. et al., “An Empirical Study of Infrasonic Propagation,” Los Alamos National Laboratory report LA-13620-MS, 1999. Stevens, J. et al., “Infrasound Scaling and Attenuation Relations from Soviet Explosion Data and Instrument Design Criteria from Experiments and Simulations,” Proceedings of the 21 st Seismic Research Symposium, Las Vegas, NV, 1999.

8. Propagation Parameterizations Azimuthal uncertainty –Theoretical formulations, based on Signal-to-noise ratio Signal and noise coherence Array geometry –Empirical formulations R. Shumway and S. Kim, “Signal Detection and Estimation of Directional Parameters for Multiple Arrays,” Defense Threat Reduction Agency Technical Report DSWA-TR-99-50, 2001. Blandford, R., “Detection and Azimuth Estimation by Infrasonic Arrays as a Function of Array Aperture and Signal Coherence,” AFTAC report, 1998. C. Szuberla, “Array Geometry and the Determination of Uncertainty,” Infrasound Technology Workshop, Kailua-Kona, HI, 2001. Clauter, D. and R. Blandford, “Capability Modeling of the Proposed International Monitoring System 60-Station Infrasonic Network,” Proceedings of the Infrasound Workshop for CTBT Monitoring, Santa Fe, NM. Los Alamos National Laboratory report LA-UR-98-56, 1997.

9. InfraMAP InfraMAP is a software tool kit –Infrasonic Modeling of Atmospheric Propagation Designed for infrasound researchers and analysts Supports infrasonic-relevant R&D –Sensitivity studies –Network performance –Modeling specific sources of interest

10. IMS Coverage Simulation Goal: –Characterize seasonally-dependent effects of stratospheric ducting on localization accuracy (AOU). Previous studies –Clauter, D. and R. Blandford, “Capability Modeling of the Proposed International Monitoring System 60-Station Infrasonic Network,” Proceedings of the Infrasound Workshop for CTBT Monitoring, Santa Fe, NM. Los Alamos National Laboratory report LA-UR-98-56, 1997. –E. Blanc and J. L. Plantet, “Detection Capability of the IMS Infrasound Network: A More Realistic Approach,” Proceedings of the Informal Workshop on Infrasounds, Bruyeres-Le-Chatel, France, 1998. Simulation parameters:

11. IMS Coverage Simulation VariableValue/DescriptionComments Background Station Noise0.5 Pa Low wind noise condition. Assume uniform across network Array configuration4 element, 1 km baseline Standard IMS array configuration Array Gain, 10log(N)6.0 dB Assume correlated signal across array Bandwidth Gain, 5log(W) Strato (W=4 Hz): 3 dB Thermo (W= 2 Hz): 1.5 dB Processing over 1 sec window Signal velocity Uncertainty Strato: 0.01 km/s Thermo: 0.02 km/s On order of that assumed in Blandford, 1998 Azimuthal Uncertainty Fit to data in Clauter and Blandford, 1997 SNR Detection Threshold2 Source Yield10kT Received Signal StrengthLANL wind-corrected eqn. Stratospheric winds at 50 km found from HWM averaged along propagation path

12. IMS Performance Number of station detections 15 0 5 10

13. IMS Performance Number of station detections 15 0 5 10

14. IMS Performance Area of Uncertainty Radius (km) 0 400 300 200 100

15. IMS Performance Area of Uncertainty Radius (km) 0 400 300 200 100

16. Conclusions Performance of IMS network strongly dependent on seasonally varying flow of stratospheric winds –Winter Northern Hemisphere:East flow Southern Hemisphere:West flow –Summer Northern Hemisphere:West flow Southern Hemisphere:East flow Localization capabilities of a given station improve in direction of stratospheric headwinds Recognized area of poor coverage: Southern Ocean Shift in “Hole” in coverage: –January:Off West coast of South America –July: Off of East coast of New Zealand AOU Radius southeast of Easter Island –January: > 400 km –July:< 100 km

17. Future Research Include station configuration –Number and location of elements –Wind filter properties Characterize local station background noise Improved characterization/modeling of propagation effects –Signal strength –Azimuthal bias and uncertainty –Signal velocity and associated uncertainty