Location and Characterization of Infrasonic Events

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Location and Characterization of Infrasonic
Events

• Roger Bowman1, Greg Beall1, Doug Drob2, Milton
  Garces3, Claus Hetzer3, Michael OBrien1, Gordon
  Shields1
• 1. Science Applications International Corporation
• 2. Naval Research Laboratory
• 3. University of Hawaii
• Infrasound Technology Workshop
• University of California, San Diego
• October 27-30, 2003

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Outline

• Challenges
• Approach
• Data sets
• Atmospheric models
• Travel-time tables
• Characterization and visualization
• Ongoing work
• Summary

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Challenges in Infrasound Monitoring
Propagation
Source
Receiver
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Location Approach
Stations
Signal observations
HWM/ MSISE models
Ray tracing (tau-p)
Travel-time tables
Location algorithm
Event locations
Uncertainty estimation
NRL G2S models
Travel-time tables
Ray tracing (tau-p)
Arrival times Azimuths
Canonical location data set
Location evaluation
Event times
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Project Network

• All stations available in June 2003

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Canonical Location Data Set

• Focuses on signals with ground truth locations
• Waveforms and arrivals
• Multiple station detections
• For assessing location, azimuth, and travel-time
  estimates
• Chemical explosions GT1-101 (3 events)
• Moving sources GT100 (3 events)
• Single station detection
• For assessing azimuth and travel-time estimates
• Mining explosions GT10-15 (5 events)
• Chemical explosions GT1-20 (5 events)
• Gas pipe explosion GS1 (1 events)
• Earthquakes GT5-10 (2 events)

1. Ground Truth with accuracy of 1 km 10 km
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Atmospheric Models
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Atmospheric Models (2)

• Meridional winds for a location in the southwest
  United States
• 0000 UT for January 1-25, 2003

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Travel-Time Tables PIDC

• Prototype International Data Center (PIDC) ca.
  2001
• Use HWM and MSISE climatological models
• Horizontal Wind Model (HWM)
• Mass Spectrometer, Incoherent Scatter Extended
  (MSISE)
• Use David Browns ray tracing program
• Include travel times for I phase only
• Depend on azimuth and season
• 1o azimuthal resolution 1.8o radial resolution
• Use uncertainties based on possible phase
  misidentification

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Travel-Time Tables Automatic Processing

• Use HWM/MSISE climatological models
• Use Milton Garces tau-p ray tracing program
• Include travel times for stratospheric (Is),
  thermospheric (It) and undetermined (I) phases
• Depend on azimuth, month and time of day
• 19 stations x 4 times of day x 12 months x 3
  phases 2,736 tables!
• 1o azimuthal resolution 1.5o radial resolution
• 0o-120o range
• Use uncertainties based on variability of G2S
  models for each month

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HWM/MSISE Travel-Time Table DLIAR

• January
• 0000 UT
• Back-azimuth 200o
• 2 out of 33,840 curves

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HWM/MSISE Travel-Time Table DLIAR

• 0000 UT
• Is phases do not exist for some azimuths
• Longer travel times westbound from source to
  receiver

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HWM Travel-Time Uncertainties

• Non-Gaussian distribution of predicted travel
  times
• Scatter in modeled travel times increases
  monotonically with range

• Characterize uncertainty by standard deviation at
  two ranges
• Interpolate for other ranges

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Accounting for Range Dependence

• Accounts for variation of atmospheric model along
  range
• Use 1-D ray tracing for four models along profile
• Final curve is 4th degree polynomial

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Travel-Time Tables Interactive Analysis

• Use Naval Research Laboratorys Ground-to-Space
  (G2S) models
• Dependent on azimuth, date and time of day
• Tables calculated for stations as needed
• Include travel times for stratospheric (Is),
  thermospheric (It) and undetermined (I) phases
• Use uncertainties based on variability of
  travel-time with take off angle for G2S models
  for each month

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G2S Travel-Time Table DLIAR

• 1000 km range
• January 23, 2003
• 2000 UT
• Similar to HWM travel times

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HWM and G2S Travel Time Tables

• 2000 km range
• January 23, 2003 2000 UT.
• January, 1800 UT
• Azimuth range for existence of Is phases differs
• All G2S travel times are larger than HWM in this
  example

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Source-Size Estimation

• Implemented Brown (1999) formula in libmagnitude
• M log10P 1.36log10R 0.019v
• Where
• P is pressure
• R is range
• v is wind velocity
• Preliminary version uses wind at infrasound
  stations from G2S model

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Visualization Tools for Characterization

• libinfra
• libPMCC
• Spectrograms

Feature Plotting
Infra Event Mapping
Array Tool
Analyst Review Station
Feature Animation

• Seismic
• Hydroacoustic
• Infrasound

• Frequency
• Apparent velocity
• Azimuth

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Infra Mapping Tool

• Supports tip-and-queue processing
• Integrated with Analyst Review Station (ARS)
• Arrival information sent back and forth
• Zoom capability
• Topography resolution varies with map scale

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Array Tool - Features

• Watusi explosion at NTS
• libPMC features
• libinfra features
• Waveforms

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Array Tool - Spectrograms

• Watusi explosion at NTS
• Standard spectrogram
• Coherence spectrogram separates coherent signal
  from incoherent noise
• Waveforms

Array Tool
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Feature Animation Tool

• Maps features to
• x-axes
• y-axes
• Color
• Saturation
• Animation sequence
• Supports 3-D animations

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Feature Animation Tool (2)
can animate over any variable
0.8 Hz
4.8 Hz
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Ongoing Work

• Location
• Test location algorithm using new travel time
  curves
• Complete travel-time tables for location event
  data set
• Quantify changes in capability to estimate
  location and azimuth
• Characterization
• Validate feature measurements
• Complete prototype analysis tool

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Summary

• Data sets
• Assembled a database of ground-truth events for
  use in evaluating infrasound source location
  estimates
• Location
• Defined a framework for using climatological and
  meteorological atmospheric models for location
  estimation
• Calculated travel-time tables based on HWM/MSISE
  for each station, month and 4 times/day
• Calculated travel-time tables based on G2S for
  each event/station in the location data set
• Enhanced location programs to accept
  station/date/time dependent travel times

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Summary (2)

• Characterization and Visualization
• Implemented source-size estimation (strongly
  dependent on wind)
• Developed prototype visualization tools for
  infrasound data feature analysis

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