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U N C L A S S I F I E D Slide 1

A Statistical Framework for Operational Infrasound Monitoring

Stephen J. Arrowsmith

Rod W. Whitaker

The views expressed here do not necessarily reflect the views of the United States Government, the United States Department of Energy, or the Los Alamos National Laboratory

LA-UR 11-03040

Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA

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Overview

  Detection: The Adaptive F-detector

  Association

  Location: The Bayesian Infrasonic Source Locator (BISL)

  Case Study: Regional Infrasound Monitoring in Utah

  Conclusions

Slide 2

InfraMonitor processing flowchart for 3 arrays

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The Adaptive F-detector

  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

  Advantages of an Adaptive F-detector: •  Does not require historical data •  Accounts for real ambient noise •  Can be applied operationally in real-time

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The Adaptive F-detector

•  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) Comparison between Adaptive F-detector and a

conventional F-detector (gray boxes denote detections)

Conventional

Adaptive

Null hypothesis violated

Null hypothesis revised based on actual noise

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Association

Slide 5

Gray areas represent grid nodes associated with test events (stars) at

three arrays

  Problem: Identify groups of N arrivals that come from the same event

  Method: Grid search over region of interest, where, for each grid node: •  Search for groups of

arrivals with backazimuths and delay-times (between arrays) consistent with that grid node

  Form associated detections for localization

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BISL

Slide 6

Currently, the complexities involved in infrasound propagation favor a statistical approach over a deterministic approach.

•  Deterministic / ray-tracing methods may not predict arrivals

•  Lacking the ability to consistently predict arrivals, the Bayesian framework enables a probabilistic formulation on phase identification

•  This information is incorporated through a Bayesian prior.

Probability density functions for phase identification

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BISL: Notation

The data used consist of:

back azimuth vector

arrival time vector

Slide 7

Model parameters consist of:

x direction x0 origin time t0

y direction y0 group velocity v

θ = [θ1, ..., θn]t = [t1, ..., tn]

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Bayes’ theorem

Using the notation introduced above, Bayes’ theorem takes the form:

Applying Bayes’ theorem requires distinguishing between a priori information and data. The former are incorporated into the Bayesian prior. The latter are incorporated into the likelihood equation.

Slide 8

Posterior PDF

Bayesianprior

Likelihood equation

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BISL: Synthetic Tests

Slide 9

  To test the algorithm, we performed a suite of tests using various synthetic configurations

  Here, we use the sample synthetic configuration shown below to illustrate the algorithm’s capabilities

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BISL: Incorporating back azimuth data

Back azimuth residual: Back azimuth likelihood component:

Slide 10

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Incorporating arrival time data

Arrival time residual: Arrival time likelihood component:

Slide 11

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Utah event

  We further test the algorithm’s performance using real infrasound data from an explosion at the UTTR test range

Slide 13

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BISL: Additional Algorithm Features

  In the case presented above, the use of Gaussian data error assumptions results in ellipsoidal credibility contours

  In general, the likelihood equations used need not be Gaussian and the credibility contours obtained need not be ellipsoidal

  Comparison of the two plots highlights the complementary nature of back azimuth and arrival time data for location constraint

Slide 15

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Case Study: Regional Infrasound Monitoring in Utah

  We have applied InfraMonitor to seven months of data from the Utah infrasound network

  82 events at 4+ arrays

  14 confirmed mining explosion detections

  1 confirmed earthquake detection (red polygon)

  Clusters of events from military facilities

Slide 16

Event locations in Utah (blue polygons)

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Case Study: The Jan 3rd, 2011 Circleville Earthquake   The earthquake was detected at 6 arrays and missed by 3

  This can be explained by propagation modeling

Slide 17

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Conclusions

  Infrasound monitoring algorithms have been developed ‘from the ground up’: •  Infrasound detection algorithms should be adaptive (accounting for

changes in ambient noise) •  Infrasound location techniques need to account for uncertainties in

atmospheric prediction models

  These techniques demonstrate that infrasound can be used for operational detection and location, at regional scales, with a low false alarm rate •  Testing of InfraMonitor in Utah missed no known ground-truth events,

detecting signals from mining explosions, ordinance disposal shots, and a magnitude 4.7 earthquake

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