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 O’Brien1, Gordon Shields1

1. Science Applications International Corporation2. 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

Source

Propagation

Receiver

Challenge Approach Lack of signals of interest Scale atmospheric nuclear explosion and

embed in ambient noise Abundance of clutter Characterize and reject clutter Time-variant propagation media

Use NRL’s G2S atmospheric models

Lack of monitoring stations Use all current stations Characterize performance of all stations Simulate detection performance of

complete network Lack of ground truth events Assemble ground truth data sets

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Location Approach

HWM/MSISEmodels

NRL G2Smodels

Ray tracing(tau-p)

Travel-time tables

Uncertaintyestimation

Stations

Canonicallocation data set

Signal observations

Location algorithm

Travel-time tables

Ray tracing(tau-p)

Event locations

Location evaluation

Event times Arr

ival

tim

esA

zim

uths

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Project Network

Acrobat Document

• 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)

HWM-93/NRLMSISE-00 G2S-E/RT

Alti

tud

e (

km)

Alti

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km)

Meridional Velocity (km/s) Meridional Velocity (km/s)

• 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 Brown’s 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

Distance

Tim

e

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Met 1at receiver

Met 2mid- dist

Met 3near max dist

2) Times shiftedbased on offset of

fits to adjacentsegments

3) Final fit toshifted points of

all segments

Illustration of rudimentary range-dependence in Tau-P ray tracing

1) Rays traced formulitple met profiles

• 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 Laboratory’s 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

Infra EventMapping

Array Tool

Feature Plotting

Feature Animation

Analyst Review Station

• Seismic• Hydroacoustic• Infrasound

• libinfra• libPMCC• Spectrograms

• 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

Array Tool

• Watusi explosion at NTS

• Standard spectrogram

• Coherence spectrogram separates coherent signal from incoherent noise

• Waveforms

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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)

0.8 Hz 4.8 Hz

…can animate over any variable

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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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