Automated infrasound signal detection algorithms implemented in MatSeis - Infra Tool.

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MatSeis's infrasound analysis tool, Infra Tool, uses frequency slowness processing to deconstruct the array data into three outputs per processing step: correlation, azimuth and slowness. Until now, an experienced analyst trained to recognize a pattern observed in outputs from signal processing manually accomplished infrasound signal detection. Our goal was to automate the process of infrasound signal detection. The critical aspect of infrasound signal detection is to identify consecutive processing steps where the azimuth is constant (flat) while the time-lag correlation of the windowed waveform is above background value. These two statements describe the arrival of a correlated set of wavefronts ... continued below

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22 p.

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Hart, Darren July 1, 2004.

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MatSeis's infrasound analysis tool, Infra Tool, uses frequency slowness processing to deconstruct the array data into three outputs per processing step: correlation, azimuth and slowness. Until now, an experienced analyst trained to recognize a pattern observed in outputs from signal processing manually accomplished infrasound signal detection. Our goal was to automate the process of infrasound signal detection. The critical aspect of infrasound signal detection is to identify consecutive processing steps where the azimuth is constant (flat) while the time-lag correlation of the windowed waveform is above background value. These two statements describe the arrival of a correlated set of wavefronts at an array. The Hough Transform and Inverse Slope methods are used to determine the representative slope for a specified number of azimuth data points. The representative slope is then used in conjunction with associated correlation value and azimuth data variance to determine if and when an infrasound signal was detected. A format for an infrasound signal detection output file is also proposed. The detection output file will list the processed array element names, followed by detection characteristics for each method. Each detection is supplied with a listing of frequency slowness processing characteristics: human time (YYYY/MM/DD HH:MM:SS.SSS), epochal time, correlation, fstat, azimuth (deg) and trace velocity (km/s). As an example, a ground truth event was processed using the four-element DLIAR infrasound array located in New Mexico. The event is known as the Watusi chemical explosion, which occurred on 2002/09/28 at 21:25:17 with an explosive yield of 38,000 lb TNT equivalent. Knowing the source and array location, the array-to-event distance was computed to be approximately 890 km. This test determined the station-to-event azimuth (281.8 and 282.1 degrees) to within 1.6 and 1.4 degrees for the Inverse Slope and Hough Transform detection algorithms, respectively, and the detection window closely correlated to the theoretical stratospheric arrival time. Further testing will be required for tuning of detection threshold parameters for different types of infrasound events.

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22 p.

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  • Report No.: SAND2004-1889
  • Grant Number: AC04-94AL85000
  • DOI: 10.2172/919172 | External Link
  • Office of Scientific & Technical Information Report Number: 919172
  • Archival Resource Key: ark:/67531/metadc888092

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  • July 1, 2004

Added to The UNT Digital Library

  • Sept. 22, 2016, 2:13 a.m.

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  • Dec. 9, 2016, 3:28 p.m.

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Hart, Darren. Automated infrasound signal detection algorithms implemented in MatSeis - Infra Tool., report, July 1, 2004; United States. (digital.library.unt.edu/ark:/67531/metadc888092/: accessed October 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.