InfraMonitor : Methodology & Application to the Utah Network

1. InfraMonitor: Methodology & Application to the Utah Network Stephen J. Arrowsmith (Los Alamos National Laboratory)

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

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

8. Association/Location • 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

9. Association/Location Observations: Predictions: • The problem can be represented by the following equations: • Where • there are n arrays, ji arrivals at the ith array, k grid nodes, and m pairs of arrays • t and Φo are observed arrival times and backazimuths at each array • dtmin, dtmax, Φp(max), and Φp(min) are bounding constraints on observations for a particular location (i.e., grid node)

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

11. Synthetic Tests Gray regions enclosed by ellipses • 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

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 Spectrogram tool Spectrum tool Detection Processing Main Window Google Earth functionality F-K Tool

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