1. Robert Gibson and David Norris Arlington, Virginia USA
Infrasound Propagation Modeling: Near-real-time Environmental Characterization and Variability Studies
Robert Gibson and David Norris
BBN Technologies
Arlington, Virginia USA
Infrasound Technology Workshop
De Bilt, The Netherlands
28-31 October 2002
Sponsored by the U.S. Defense Threat Reduction Agency 
Contract Nos. DTRA01-01-C-0084 and DTRA01-00-C-0063

2. InfraMAP Objectives
Integrate the software tools needed to perform infrasound propagation modeling.
Define the state of the atmosphere (temperature, wind) as required for input to propagation models.
Predict phases (travel times, bearings, amplitudes) from atmospheric explosions as measured by infrasound sensors.
Apply tools to support nuclear explosion monitoring R&D and resolve operational issues:
environmental variability and propagation sensitivity
prediction of localization area of uncertainty and detection performance
performance model of an infrasound network

3. InfraMAP

4. Environmental Analysis
Empirical characterizations are built-in
User-defined profiles can be imported
View Environment functions via GUI
Seamless integration with propagation models

5. Propagation Analysis

6. Variability Analysis

7. Motivation for Current Work
Evaluate methods to improve infrasound localization and phase identification.
Apply advanced modeling to investigate improvements to operational processing.
Recommendations from the 2001 Infrasound Technology Workshop:
“Routine processing needs to incorporate more precise estimates of the atmospheric state.”
“Support integration of required environmental information for modeling … leading to final goal of an operational full modeling capability.”
Support infrasound R&D community by maintaining InfraMAP as user-friendly modeling tool kit.

8. InfraMAP Software Schematic
Environmental
Characterization
MSIS-90
HWM-93
User-defined profile
Environmental Variability
NRL-MSIS-00
Next-Generation HWM
Ground-to-Space Assimilation
Propagation
Modeling
Ray Tracing
Normal Mode
Parabolic Equation
Network Performance
Near-Real-Time Updates
Model-based assimilation
In-situ measurements
Solar/Magnetic parameters
Date
Time
Latitude
Longitude
Altitude
Model
Parameters
Azimuth Deviation
Travel Time
Elevation Angle
Attenuation
Synthetic Waveform
from modes
Variability Statistics
Environment
Source Localization
Area of Uncertainty
Synthetic Phases
from rays
GUI

9. Approach and Applications
Incorporate updated atmospheric characterizations
Evaluate best available sources
Integrate with propagation models and user-interfaces
Conduct sensitivity studies
Apply propagation statistics to localization problem
Model bias and variance
Investigate techniques to refine source localization
Predict uncertainty and network performance
Model observed infrasound events
Obtain ground truth source location
Compare model results with observations
Assess quality of atmospheric characterization

10. Numerical Weather Prediction
Physics-based models that assimilate data from many sources
Grids produced 2 to 4 times per day by various organizations
Interpolation (3-d) and merging (in altitude) are required
Example: NOGAPS from US Navy’s Fleet Numerical Meteorology and Oceanography Center (FNMOC)
Navy Operational Global Atmospheric Prediction System
NOGAPS grid is nonuniform in altitude. Highest available level is typically at 10 mb atmospheric pressure
Figures at right show temperatures along a slice of the atmosphere at fixed longitude (60 W)
Top: MSIS-00
Bottom: NOGAPS

11. NOGAPS Gridded Data Example
Left: Zonal Wind
Right: Meridional Wind
Top: HWM-93.
Bottom: NOGAPS

12. Gridded Data Incorporation
Issue: Ray models are highly sensitive to sharp changes in sound speed. Therefore, estimation approach must avoid introducing gradient variability not inherent to the data set.
Approach: A cubic spline algorithm is being developed to interpolate gridded data and to merge grid with empirical models.
Cubic spline interpolation provides smooth 1st derivative and continuous 2nd derivative, assuring compatibility with ray modeling.
Temperature (T) Zonal (U) Meridional (V)
Spline through NOGAPS points
HWM
MSIS
Derivatives at endpoints
Transition layer

13. Range Dependence
Below: Integrated winds for ten latitude cells at a fixed longitude (60 W)
White lines indicate 10 km transition zone between NOGAPS and HWM
Zonal (U) Meridional (V)

14. Ray Tracing Example
Search for stratospheric eigenrays over 1000 km eastward path in S. Am.
IS41, Villa Florida, Paraguay. 25 July 2002, 0:00 UT.
Left: MSIS/HWM Right: NOGAPS 3-d grid
IS41
NOGAPS grid
subset
In this case, use of near-real-time grid reduces stratospheric turning height and introduces additional duct below 10 km.

15. Environment for Example Case
Sample profiles from modeled scenario:
NOGAPS and 10 km transition layer in blue, MSIS/HWM in red

16. Pacific Bolide: Baseline Localization
Event
Bolide 23 Apr 2001
Ground Truth determined using satellites
Baseline localization from CMR study of detections at 6 stations using seasonal velocity model
Brown and Gault, CMR Tech Report CMR-01/09, 2001
Key features
MLE algorithm using both measured back azimuths and arrival times
Signal velocities estimated from season-based velocity model
Large travel time variance specified to account for ray class (Stratospheric, Thermospheric) uncertainty
No correction for azimuthal deviation due to horizontal translation/refraction of ray paths
Baseline localization
Ground truth on edge of AOU
Ground truth/localization mismatch: 138 km
AOU: 85,400 sq km
IS57
NVIAR
DLIAR
IS59
IS10
IS26
Source
Baseline localization
Ground Truth

17. Model-Enhanced Approach
Baseline localization used as initial data point for modeling.
Ray traces computed to identify ray class (stratospheric or thermospheric) that is most consistent with baseline localization.
For identified ray class, mean signal velocity and azimuthal deviation computed and used in new localization.
Atmospheric variability accounted for by using spectral gravity wave model. Propagation variability due to environment found by performing Monte Carlo simulation in which rays are propagated through perturbed realizations of atmosphere.
Total uncertainties (travel time variance, azimuth variance) computed by combining ray variability within ray class, propagation variability due to environment, and measurement error:
Azimuthal variance dominated by measurement resolution
Travel time variance dominated by ray variability within ray class
Model-enhanced localization found from same baseline MLE algorithm but using:
New signal velocity estimates
Back azimuth measurements corrected by azimuthal deviation estimates
New estimates for variances of travel time and azimuth
Ray classification details ->

18. Diffracted Stratospheric rays
Ray Classification
Thermospheric rays
Diffracted Stratospheric rays
Reverse propagate fan of rays from receivers to baseline localization
Group rays in classes: Thermospheric, Diffracted Stratospheric
Diffracted Stratospheric ray class
Rays that are trapped in elevated duct 10 km above ground
Assume ray reaches receiver through diffraction/scatter of energy from 10 km height
IS59
IS59
Ray classification achieved by comparing mean Signal Velocity and Azimuthal Deviation within a ray class with that found using baseline localization
IS59 example: Signal velocity used in baseline localization well modeled by mean value of diffracted stratospheric rays

19. Ray Classification Observations
Station
Ray Class
Sig Vel [km/s]
Az Dev [deg]
(TT) [s]
(Az) [deg]
IS57
Diff Strato
0.294
0.1 37
NVIAR
0.295
-0.4 49
IS59
0.289
1.0 73
0.2
DLIAR
0.3 71
IS10
0.296
-1.1 79
IS26
Thermo
0.254
0.9
717
Ray class IDs, and associated mean and standard deviations
Signal velocity found to be better discriminator than azimuthal deviation in this case
Classification approach did not clearly indicate a dominant ray class in all cases
More research needs to be done
Ray classification over multiple iterations of source localizations
Use of additional discriminators (phase arrival envelope, amplitude, etc.)

20. Model-Enhanced Localization
New localization found using same MLE algorithm as was used for baseline localization but with:
New signal velocity estimates
Back azimuth measurements corrected by azimuthal deviation
New summed estimates for variances of travel time and azimuth
Key improvements
More accurate signal velocity estimates
Smaller uncertainty in travel time
Model-enhanced Localization
Ground truth again on edge of AOU
Ground truth/localization mismatch: 85 km
AOU: 9834 sq km
Baseline localization
Model-enhanced localization
Ground Truth
Final localization accuracy increased by 38% with AOU decreased by 88%

21. Case Study: Etna Eruption
Explosive eruption 2001-July-29
~0600 GMT
Observed at Deelen, NL and elsewhere in Europe
Ground truth from Deelen:
~1750 km
152.4 degrees
Observed azimuth of arrival
degrees
~ +2.5 to +3.5 degrees of azimuth deviation
Data analysis by L. Evers (KNMI)
Predicted azimuth using InfraMAP
156.4 degrees; +4 degrees deviation
Thermospheric paths
Using empirical atmospheric models
Little change using NWP model up to 35 km

22. Space Shuttle Modeling Approach
Source of infrasound is not impulsive, but continuous and moving
Approximate moving source by modeling a series of discrete events, each with appropriate time delay
Use 3-D ray tracing to find eigenrays from points on launch trajectory to infrasound array
Orbiter
Solid rocket boosters (SRB)
Determine arrival time and azimuth for each eigenray
Example of eigenrays from one point on trajectory to array location

23. Shuttle STS-96 at IS10 - InfraTool

24. Shuttle STS-96 – Data at IS10

25. Shuttle STS-96 – Model to IS10

26. Conclusions
Software models are being successfully used to predict travel time and bearing biases for atmospheric infrasound.
Empirical atmospheric modeling (climatology) is adequate for many scenarios, but not all.
Software development is underway to incorporate output of numerical weather prediction models into InfraMAP.
Propagation statistics are being incorporated into localization, AOU, and network performance predictions.
Rocket launches are a promising source of ongoing calibration data to validate environmental characterizations.