Collaborators

Rod Whitaker, George Randall [Los Alamos National Laboratory]

Relu Burlacu [University of Utah]

Chris Hayward, Brian Stump [Southern Methodist University]

Overview

Motivation

Signal Detection

Association/Location

Synthetic Tests

InfraMonitor 2.0

Application to the Utah network

Summary

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

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

• 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)

Signal Detection

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cF2BT ,2BT (N −1)

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c = 1 + NPsPn

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⎝ ⎜ ⎞

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SignalDetectionPinedale, Wyoming data

Symbols: Adaptive detector (stars), Conventional (circles), infrasound (filled), seismic (open)

Adaptive window: 1 hour

Adaptive window: 24 hours

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

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Δd =GΔm

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)

Association/Location

Observations:

Predictions:

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t =

t11 L t1 j1( )M

tn1 L tnjn( )

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⎨ ⎪

⎩ ⎪

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⎬ ⎪

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Φ o =

φ11o L φ1 j1

o( )

M

φn1o L φnjn

o( )

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⎨ ⎪

⎩ ⎪

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⎬ ⎪

⎭ ⎪

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dtmax =

dt11max L dt1m

max

M O M

dtk1max L dtkm

max

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dtmin =

dt11min L dt1m

min

M O M

dtk1min L dtkm

min

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Φ p(max) =

φ11p(max) L φ1m

p(max)

M O M

φk1p(max) L φkm

p(max)

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⎜ ⎜

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Φ p(min) =

φ11p(min) L φ1m

p(min)

M O M

φk1p(min) L φkm

p(min)

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⎜ ⎜

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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 (vmin – vmax), we must search for associated arrivals where the apparent velocity (vapp) is, for all array pairs:

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d2 − d1

d2

vmin

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⎠ ⎟−d1

vmax

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≤ vapp ≤d2 − d1

d2

vmax

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⎠ ⎟−d1

vmin

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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

Δφ=6°,vg = 0.28 − 0.34km / s

Δφ=3°,vg = 0.32 − 0.34km / s Δφ=1°,vg = 0.299 − 0.301km / s

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

InfraMonitor 2.0

Main Window

DetectionProcessing

F-K Tool

Spectrogramtool

Spectrumtool

Google Earthfunctionality

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.

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

Infrasound Events

Ground-truth association of event locations with satellite imagery from Google Earth

Event 1: Ground-truth Explosion

Event 2: Suspected Explosion

Topography blockageAt NOQ?

Event 3: Wells Earthquake

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