A Statistical Framework for Operational Infrasound Monitoring

Transcription

1 A Statistical Framework for Operational Infrasound Monitoring Stephen J. Arrowsmith Rod W. Whitaker LA-UR 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 Slide 1

2 Overview Detection: The Adaptive F-detector Association Location: The Bayesian Infrasonic Source Locator (BISL) Case Study: Regional Infrasound Monitoring in Utah Conclusions InfraMonitor processing flowchart for 3 arrays Slide 2

3 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

4 The Adaptive F-detector Shumway et al. (1999): In the presence of stochastic correlated noise, F-statistic is distributed as: Null hypothesis violated Where: Conventional To estimate c (i.e., Ps/Pn), adaptively fit F distribution peak to Central F- distribution peak while processing data Null hypothesis revised based on actual noise Adaptive Apply p-value detection threshold (e.g., p = 0.01) Comparison between Adaptive F-detector and a conventional F-detector (gray boxes denote detections)

5 Association 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 Gray areas represent grid nodes associated with test events (stars) at three arrays Slide 5

6 BISL 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 Slide 6

7 BISL: Notation The data used consist of: back azimuth vector arrival time vector θ =[θ 1,...,θ n ] t =[t 1,...,t n ] Model parameters consist of: x direction x 0 origin time t 0 y direction y 0 group velocity v Slide 7

8 Bayes theorem Using the notation introduced above, Bayes theorem takes the form: Posterior PDF Bayesian prior Likelihood equation 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

9 BISL: Synthetic Tests 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 Slide 9

10 BISL: Incorporating back azimuth data Back azimuth residual: Back azimuth likelihood component: Slide 10

11 Incorporating arrival time data Arrival time residual: Arrival time likelihood component: Slide 11

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13 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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15 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

16 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 Event locations in Utah (blue polygons) Slide 16

17 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 UNCLASSIFIED Slide 17

18 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 Slide 18