2. Collaborators  Rod Whitaker, George Randall [Los Alamos National Laboratory]  Relu Burlacu [University of Utah]  Chris Hayward, Brian Stump [Southern Methodist University]

3. Overview  Motivation  Signal Detection  Association/Loca tion  Synthetic Tests  InfraMonitor 2.0  Application to the Utah network  Summary

4. Motivation  Infrasound research has been largely event-driven by:  Direct ground-truth  Ground-truth from seismology, satellites  There is a need for a fully-integrated technique for automatic regional infrasound monitoring  Infrasound Data  InfraMonitor  Event Catalogs  Historically, techniques for processing infrasound data are borrowed from seismology  But, infrasound monitoring requires different strategies due to unique challenges  Temporal variability of medium  Noise issues

5. Signal Detection  The human eye is remarkably competent at detecting signals in noisy data, automatic algorithms must attempt to match this level of capability  Requirement: Hypothesis that can be tested  Standard hypothesis: Noise is spatially incoherent  This is frequently violated, leading to large numbers of spurious ‘signals’  This hypothesis does not adapt to variations in ambient noise  We have developed coherent and incoherent detectors with the following criteria:  Does not require historical data  Accounts for real ambient noise  Can be applied operationally in near real-time  Thus, a sensor or array can be deployed in a new region and the automatic detector applied immediately

6. Shumway et al. (1999): In the presence of stochastic correlated noise, F-statistic is distributed as: Where: To estimate c (i.e., P s /P n ), adaptively fit F distribution peak to Central F- distribution peak while processing data Apply p-value detection threshold (e.g., p = 0.01) Signal Detection

7. Pinedale, Wyoming data Symbols: Adaptive detector (stars), Conventional (circles), infrasound (filled), seismic (open) Adaptive window: 1 hour Adaptive window: 24 hours

8.  Seismic location techniques typically use an inverse approach (Geiger’s method):  This method requires a model  Unfortunately, state-of-the-art 4D atmospheric models:  Have not been validated at local or regional scales  Do not always predict observed phases  We have developed a new forward technique that:  Places bounding constraints on location (producing location polygons)  Does not require a model Association/Location

9.  The problem can be represented by the following equations:  Where  there are n arrays, j i arrivals at the i th array, k grid nodes, and m pairs of arrays  t and Φ o are observed arrival times and backazimuths at each array  dt min, dt max, Φ p(max), and Φ p(min) are bounding constraints on observations for a particular location (i.e., grid node) Association/Location Observations:Predictions :

10. Association/Location  Consider a pair of arrays, Arrays 1 and 2, and corresponding grid node, k:  If we are searching for any phase within a specified group velocity range (v min – v max ), we must search for associated arrivals where the apparent velocity (v app ) is, for all array pairs:

11. Synthetic Tests Synthetic Tests provide Test of algorithm/code assessment of network resolution In each panel Stars show locations of synthetic events Gray regions show localization uncertainty Search parameters represent uncertainty in propagation Gray regions enclosed by ellipses

12. InfraMonitor 2.0  Features:  GUI interface for interactive data analysis  Command-line functions for batch data processing  Seamless integration of detection, association, and location methodologies  CSS3.0 compatible  Requirements:  Matlab  + Signal Processing Toolbox  + Mapping Toolbox  + Statistics Toolbox

13. InfraMonitor 2.0 Main Window Detection Processing F-K Tool Spectrogram tool Spectrum tool Google Earth functionality

14. Utah Seismo-acoustic Network  Operated by the University of Utah Seismograph Stations (UUSS)  Designed to record seismo-acoustic signals from rocket motor detonations in northern Utah.  The arrays are co-located with UUSS seismic stations  100 m aperture arrays  Porous hoses for noise reduction.

15. Infrasound + Seismo-acoustic Events  Duration of Study: 1 month (Summer)  Parameters optimized for high-frequency arrivals  287 infrasound events  12 seismo-acoustic events  Analyst Review of all 287 events indicates false alarms make up <25% of the total  4 ground-truth rocket motor shots are all detected seismo-acoustically

16. Infrasound Events Ground-truth association of event locations with satellite imagery from Google Earth

17. Event 1: Ground-truth Explosion

18. Event 2: Suspected Explosion Topography blockage At NOQ?

19. Event 3: Wells Earthquake

20. Summary  New methods for detection and location of regional infrasound events have been developed  Detector: Accounts for temporally-variable correlated noise  Locator: Bounding approach does not require a model  Techniques have been validated using synthetic tests and Utah network data  Analyst review of Utah events suggests a low false association rate (<25 %)  Events from earthquakes, explosions (military + mining), and numerous other sources are detected  InfraMonitor 2.0 integrates detection, association and location algorithms seamlessly into a Matlab toolbox