Assessing the detection capability of the International Monitoring System infrasound network

British Crown Copyright 2008/MOD

Assessing the detection capability of the International Monitoring System infrasound network

David Green and David Bowers

AWE Blacknest


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


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)


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


The Model

• Calculate the probability, Pijk, 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.

Pdetect = 1 − Pno detect − Pone detec

Prob. of detection Probability of no detectionacross network

Probability of detection atonly one station across network


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 signaland noise amplitude

The predicted signal-to-noiseratio at the station.

The signal-to-noise level atwhich a detection can bemade divided by signal-to-noise improvement from beamforming.

We use a single channel signal-to-noise ratio = 1 for thecalculations shown.

(log10-log10)


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 5th and 95th 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 anecessary future improvement (already implemented byLe Pichon et al, 2008).


Array Processing: Signal-to-Noise Improvements

Neyshabur Train Explosion recorded at I31KZ (1 to 5 Hz)

Christmas Island BolideRecorded at I04AU (0.1 to 0.5Hz)

Beam

Single Channel

Signal Window Pre-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



• 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 Receiverrange = 1000km

• Can combine with the Whitaker Yield vs. Amplitude relation to give Frequency vs. Amplitude relation.


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.


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.


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


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



(Noise from AFTAC relation – indicates that the frequency at which the noise is taken from theBowman 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


Comparison with Gerdec Explosion Observations

Dom.SignalFreq.

Gerdec, Albania:• 2 large munitions dump explosions• 15th 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

Decreasingyield

=Decreasingprobability ofsingle stationdetection.


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 tosimulate deterministicapproach.

A more deterministicapproach.

Can compare and contrast techniques; underlying empirical models are identical


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. Pressure2. Yield vs. Period


References and Acknowledgements

Acknowledgements

Fruitful discussions with Alexis Le Pichon, Lars Ceranna, Laslo Evers and JulienVergoz helped improve this work, and highlighted possible avenues for future work.

We thank Roger Bowman (SAIC) for providing us with the noise model across theIMS infrasound network.

Green, D. N., Assessing the detection capability of the International Monitoring System infrasound network AWE Report 629/08 (2008) (Available on request, [email protected])

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: 2003-2006Infrasound 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 Scalingand 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).


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:

Assessing the detection capability of the International Monitoring System infrasound network

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