1. Page 1 British Crown Copyright 2008/MOD Assessing the detection capability of the International Monitoring System infrasound network David Green and David Bowers AWE Blacknest

2. Page 2 British Crown Copyright 2008/MOD Assessing the detection capability of the International Monitoring System infrasound network AWE Blacknest Talk Outline David Green and David Bowers 1. Previous Models 2. Extensions - Noise - Array Processing - Stratospheric Wind - Frequency Dependence 3. Results 4. Comparison to observed: Gerdec Explosions

3. Page 3 British Crown Copyright 2008/MOD Previous Work IMS infrasound network 60 stations when complete 37 arrays as of July 2007 +( ) Previous models: incorporate Yield vs. Amplitude relationships incorporate noise measurements no wind incorporated (e.g., Clauter and Blandford, 1998, Stevens et al. 2002) (following Stevens et al. 2002)

4. Page 4 British Crown Copyright 2008/MOD Extended Model Components Noise (Bowman et. al, 2007) Array Processing SNR enhancements Amplitude dependence on: 1. Yield 2. Stratospheric Wind Conditions (Whitaker et al. 2003) Frequency Dependence (Noise Models and Yield Relationships) Network Completeness Detection Capability

5. Page 5 British Crown Copyright 2008/MOD The Model Calculate the probability, P ijk, that a signal will be detected at station i for an event k occurring at location j. Assume that noise distribution is taken as log-normal (after e.g., Clauter and Blandford, 1998) We define the detection threshold as the yield at which the probability of detecting an event at two or more stations in the network exceeds 0.9. P detect = 1 − P no detect − P one detec Prob. of detection Probability of no detection across network Probability of detection at only one station across network

6. Page 6 British Crown Copyright 2008/MOD The Model The probability that a signal will be detected at station i for an event k occurring at location j. Station reliability Uncertainities in signal and noise amplitude The predicted signal-to-noise ratio at the station. The signal-to-noise level at which a detection can be made divided by signal-to-noise improvement from beamforming. We use a single channel signal-to-noise ratio = 1 for the calculations shown. (log 10 -log 10 )

7. Page 7 British Crown Copyright 2008/MOD Noise Models Used (Bowman et. al, 2007) Used noise models from Bowman et. al, 2007 An analysis of 39 stations (34 IMS) - 44 months of data used Figure to right shows median spectra (solid line) and the 5 th and 95 th percentile spectra (dotted lines). In our detection capability models we assume time-independent, station-independent noise model Extending to time and location dependent models is a necessary future improvement (already implemented by Le Pichon et al, 2008).

8. Page 8 British Crown Copyright 2008/MOD Array Processing: Signal-to-Noise Improvements Neyshabur Train Explosion recorded at I31KZ (1 to 5 Hz) Christmas Island Bolide Recorded at I04AU (0.1 to 0.5Hz) Beam Single Channel Signal WindowPre-event noise A study of 6 events showed gains of between 0.7 and 1.1 x √n (where n = no. of array elements) For all modelling, used value of 0.9√n

9. Page 9 British Crown Copyright 2008/MOD Frequency Dependence At what frequency should we take our noise estimates? Are different size sources going to be preferentially observed at different frequencies? or e.g., AFTAC Yield (Y) vs. Period (t) Equation Source to Receiver range = 1000km Can combine with the Whitaker Yield vs. Amplitude relation to give Frequency vs. Amplitude relation.

10. Page 10 British Crown Copyright 2008/MOD Adding the Stratospheric Wind: 59 Station Network 90% Detection Threshold (2 station) for the 59 station network. Noise from Bowman (2007) model, taken at 0.1Hz.

11. Page 11 British Crown Copyright 2008/MOD 37 Station Network (Operational in 2007) 90% Detection Threshold (2 stat.) for the 37 station network. Noise from Bowman (2007) model, taken at 0.1Hz.

12. Page 12 British Crown Copyright 2008/MOD Comparing 37 and 59 Station Networks New York, 37 Stat. New York, 59 Stat. Nov. Zemlya, 59 Stat. Nov. Zemlya, 37 Stat. Incomplete network – decrease detection prob. at specific locations value dependent upon: 1. the completeness of the regional network 2. the influence of the dominant wind directions. Solid lines: with wind Dotted lines: without wind

13. Page 13 British Crown Copyright 2008/MOD Location Capability : Influence of Stratospheric Wind With less wind - the angle of separation of the two detecting stations tends to be greater - the source to second receiver distance tends to be less No Wind High Wind Including wind – better detection capability, but apparently harder to locate source

14. Page 14 British Crown Copyright 2008/MOD Frequency Dependence (Noise from AFTAC relation – indicates that the frequency at which the noise is taken from the Bowman et. al (2007) model is determined using the AFTAC yield vs. period relation). If noise variation with frequency is taken into account: - at low yields, achieve better global detections because of lower noise - if comparing with a single noise value (at 0.1Hz), only at the microbarom peak does the variable noise model perform less well than the frequency varying noise Equivalent diagrams

15. Page 15 British Crown Copyright 2008/MOD Comparison with Gerdec Explosion Observations Dom. Signal Freq. Gerdec, Albania: 2 large munitions dump explosions 15 th March 2008 Highest SNR ~ 0.5Hz Signal Power down to periods of ~30s Future IMS station Detecting IMS station Detecting non-IMS Non-detecting non-IMS Decreasing yield = Decreasing probability of single station detection.

16. Page 16 British Crown Copyright 2008/MOD Comparison with Gerdec Explosion Observations Green and Bowers, 2008 Le Pichon et al., 2008 Tons (TNT) (see Alexis’ talk) Two independent models. Converge to very similar results. Adapted to incorporate: ECMWF wind model measured noise levels Uncertainties reduced to simulate deterministic approach. A more deterministic approach. Can compare and contrast techniques; underlying empirical models are identical

17. Page 17 British Crown Copyright 2008/MOD Conclusions Inclusion of stratospheric wind makes detection capability time dependent. Inclusion of stratospheric wind tends to improve detection capability, but hinders location capability for low yield explosions. The completeness of the IMS network is vital for ensuring global coverage. The frequencies of interest should be considered when calculating the network detection capability. Model presented here is relatively simple in design; future improvements will include: More accurate atmospheric parameterisation Array-dependent wind noise Improved understanding of 1. Yield vs. Pressure 2. Yield vs. Period

18. Page 18 British Crown Copyright 2008/MOD References and Acknowledgements Acknowledgements Fruitful discussions with Alexis Le Pichon, Lars Ceranna, Laslo Evers and Julien Vergoz helped improve this work, and highlighted possible avenues for future work. We thank Roger Bowman (SAIC) for providing us with the noise model across the IMS infrasound network. Green, D. N., Assessing the detection capability of the International Monitoring System infrasound network AWE Report 629/08 (2008) (Available on request, dgreen@blacknest.gov.uk) Le Pichon, A. et al. Assessing the performance of the International Monitoring System infrasound network: Geographical coverage and temporal variabilities JGR – Atmospheres (in press, 2008) Bowman, J. R. et al. Infrasound Station Ambient Noise Estimates and Models: 2003-2006 Infrasound Technology Workshop, Tokyo, November 2007 Clauter, D. and Blandford, R. Capability Modelling of the Proposed International Monitoring System 60-Station Infrasonic Network. In Infrasound Workshop for CTBT, pages 215–225, Santa Fe, New Mexico, USA, August 25-28, 1997, (1998) LANL. LA-UR-98-56. Stevens, J.L., Divnov, I.I., Adams, D.A., Murphy, J.R., and Bourchik, V.N. Constraints on Infrasound Scaling and Attenuation Relations from Soviet Explosion Data. Pageoph, 159, 1045–1062, (2002). Whitaker, R. W., Sondoval, T. D., and Mutschlecner, J. P. Recent Infrasound Analysis. In Proceedings of the 25th Annual Seismic Research Symposium in Tuscon, AZ, pages 646–654, (2003).

19. Page 19 British Crown Copyright 2008/MOD From Probabilistic to Deterministic When comparing Le Pichon et al. (2008), and Green and Bowers (2008) ‘We are confident that we can predict noise levels and stratospheric wind values’ and ‘We are confident that our empirical models provide the correct yield/pressure relationship’ Problem: how to correctly assess the uncertainties. By moving towards the deterministic model, we are saying: