Infrasound Technology Workshop Bermuda, 2008 Enhancements to the CTBTO operational automatic infrasound processing system David Brown, Nicolas Brachet, and Ronan Le Bras International Data Centre Software Applications Section Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization Provisional Technical Secretariat Vienna International Centre P.O. Box 1200, A-1400 Vienna, Austria

Infrasound Technology Workshop, Bermuda November 2008 Page 2 Overview  Station Noise characterization  purpose  requirements  method  testing  real data  still to be done  Infrasound amplitude determination  purpose  procedure  testing  still to be done Two enhancements to the IDC automatic infrasound processing system are being tested:

Infrasound Technology Workshop, Bermuda November 2008 Page 3 Station Noise Characterization Purpose  Provide a method for users of IDC external products to determine station ‘noise’ levels. will be useful in determining network detection capability  Provide an additional utility for internal PTS station performance monitoring. Requirements  Noise data for each station would need to reveal: seasonal or monthly variations day and night time variations  External/Internal Users need to access the binary data and plot files. Method  Employ Power Spectral Density methods along the lines of the analysis of Bowman et al. will run automatically on incoming data and update seasonal or monthly averages.

Infrasound Technology Workshop, Bermuda November 2008 Page 4 Choose 4 hourly periods for each station:  03:30-04:30, 09:30-10:30, 15:30-16:30, 21:30-22:30 local time Choose a FFT window function:  References: Harris 1978: On the use of Windows for Harmonic Analysis with the Discrete Fourier Transform Proc. IEEE Vol 66, No. 1, Heinzel 2002: hannover.de/?page=publikationen&sub=publikationen&type=publi&publitype=3&lang=enhttps:// hannover.de/?page=publikationen&sub=publikationen&type=publi&publitype=3&lang=en  Require moderate frequency resolution  Require good amplitude resolution Station Noise Characterization

Infrasound Technology Workshop, Bermuda November 2008 Page 5 no window equivalent to High Frequencies Windowing:  a significant part of the PSD measurement process Station Noise Characterization

Infrasound Technology Workshop, Bermuda November 2008 Page 6 ‘Dirac Comb’ spectral leakage Windowing:  non-periodic nature of the windowing process leads to spectral leakage  frequency content and spectral leakage controlled by the transform of the window function: Station Noise Characterization

Infrasound Technology Workshop, Bermuda November 2008 Page 7 Windowing :  Ensure the side lobes of the Window Transform are below the noise floor of the sensor system (microbarometer + filter): MB2005: approx Pa 2 /Hz → Nutall4a window (Heinzel et al, 2002) Chaparral : approx Pa 2 /Hz → Nutall4c window (Heinzel et al, 2002) Station Noise Characterization

Infrasound Technology Workshop, Bermuda November 2008 Page 8 ‘Welch’ PSD method  Divide hourly data into overlapping 3 minute windows without averaging, the variance of the PSD for a individual frequency picket is of the order of the PSD at that frequency. 20 Hz sample-rate →3600 samples or 1800 frequency pickets: Hz→ DC Hz → 640 sec period Hz → 320 sec period … Hz … one hour Station Noise Characterization

Infrasound Technology Workshop, Bermuda November 2008 Page 9 windowROV % 3-minute windows per hour ENBW (Equivalent Noise Band Width, bins) side lobe level (db) emax % 3db width (bins) high frequency resolution windows Rectangular general purpose windows Welch Hanning Hamming Nutall4a Nutall4c high amplitude resolution windows FTSRS HFT248D  Number of overlapping windows is determined by the window ROV (Relative Overlap Value, see Heinzel et al, 2002), indicated in the following Table: Station Noise Characterization

Infrasound Technology Workshop, Bermuda November 2008 Page 10  Remove linear trend from each 3 minute data segment otherwise DC component may generate spectral leakage  Multiply data segment with window function remember to divide by the processing gain, ie, multiply by  Apply FFT  Determine PSD at frequency picket i according to the rule :  average the PSD for all sub windows and take the log of the average could consider taking the average of the log’s, but Bowman et al’s work suggests not log-normal distribution. N = samples;  = sample frequency;  = ENBW Accommodates the accumulation ability of the main lobe for white noise Station Noise Characterization

Infrasound Technology Workshop, Bermuda November 2008 Page 11 Testing  use artificial digitizer noise Digitizing process generates (white) noise power spectral density: (see Lyons, 1997; Heinzel, 2002) is the Pa corresponding to one least-significant bit is the sampling frequency, Hz.  Example: with Station Noise Characterization

Infrasound Technology Workshop, Bermuda November 2008 Page Testing  use 4 different windows:  Rectangular (no window)  Hanning  Nutall4a (Heinzel 2002)  HFT248D (Heinzel 2002) Station Noise Characterization

Infrasound Technology Workshop, Bermuda November 2008 Page 13 Testing  have initiated a series of ‘blind-tests’ with Lars Ceranna at BGR to assess the validity of the method. using randomly chosen IMS array data IS07, IS27, IS32, IS , 1 st day of the month 4 times per day. will ‘manipulate’ the PSD methods until we get the same answers.  compare with known published results: Bowman et al. Station Noise Characterization

Real Data  PSD noise data is being determined automatically for every IMS infrasound station.  Two outputs are generated: Output 1: log-average PSD data for the given hours binary data file + plot (.ps) should be free of contamination from spectral leakage have complete hourly and monthly-averaged PSD information for each station for September 2008, October 2008 and the current part of November for each 8-element station the data growth rate is : 4 x ( ) bytes = 840Kb (.jpg binary) Infrasound Technology Workshop, Bermuda November 2008 Page 14 Station Noise Characterization

Infrasound Technology Workshop, Bermuda November 2008 Page 15 Real Data  PSD noise data is being determined automatically for every IMS infrasound station.  Two outputs are generated: Output 1: log-average PSD data for the given hours binary data file + plot (.ps) should be free of contamination from spectral leakage have complete hourly and monthly-averaged PSD information for each station for September 2008, October 2008 and the current part of November for each 8-element station the data growth rate is: 4 x ( ) bytes = 520Kb / day (approx. 5 Gb per year for 60-stations) (generate graphic on the fly) Station Noise Characterization

Infrasound Technology Workshop, Bermuda November 2008 Page 16 Real Data  PSD noise data is being determined automatically for every IMS infrasound station.  Two outputs are generated: Output 2: monthly-average PSD data + variance smoothed using an 11-point 6-order Savitzky- Golay Filter for the specified times. Savitzky-Golay filter o polynomial regression that preserves features of the distribution such as relative maxima, minima and width have complete hourly and monthly-averaged PSD information for each station for September 2008, October 2008 and the current part of November Station Noise Characterization

Infrasound Technology Workshop, Bermuda November 2008 Page 17 Still to be done  incorporate as part of mainstream IDC processing  make data accessible to external users via a web-service interface  incorporate into Station Performance Monitoring tool for internal PTS use Station Noise Characterization

Infrasound Technology Workshop, Bermuda November 2008 Page 18 Infrasound Amplitude Determination Purpose  Provide a method for determining infrasound amplitudes in Pascals useful information to write in the Bulletin may be useful in Network processing

Infrasound Technology Workshop, Bermuda November 2008 Page 19 Procedure  Apply Window Function in the time domain Use FTSRS ‘Flat Top’window process sufficient data such that the window function will not drop by more than 50% from its peak value over the duration of the signal segment. otherwise signal will get mangled by the window division processes signal start time signal end time signal window function extra data Infrasound Amplitude Determination

Infrasound Technology Workshop, Bermuda November 2008 Page 20 Infrasound Amplitude Determination Procedure  Apply FIR filter two filter bands chosen: 0.5 to 3.0 Hz minfreq and maxfreq determined by pmcc detection algorithm  Remove window function from signal segment by dividing  Three amplitude measures applied to a time-aligned beam : beam aligned according to azimuth + slowness in CSS detection table Peak-to-Peak: used in several attenuation laws LANL DASE RMS (user specifiable window length) Amplitude of the Analytic Trace (see Olson, 2000)  For each detection: 6 amplitude measures are being determined the Peak-to-Peak Hz amplitude is being written to the CSS arrival table, all others are being written to the amplitude table.

Infrasound Technology Workshop, Bermuda November 2008 Page 21 Infrasound Amplitude Determination Testing  A sinc function is used to test beamformer, filtering procedure and amplitude measurement Peak-to-Peak (P-P) amplitude = ; Analytic amplitude = 2.0 Nutall 4a window Hz Hz Overlapping traces: beamformer is working correctly filter process is not generating a phase-shift window division is not mangling the data non-overlapping traces: filter process is not generating a phase-shift window division is not mangling the data

Infrasound Technology Workshop, Bermuda November 2008 Page 22 windowamplitude measure0.001 to Hz0.15 to 0.6 Hz rectangle P-P RMS Analytic welch P-P RMS Analytic hanning P-P RMS Analytic nutall4a P-P RMS Analytic HFT248D P-P RMS Analytic Testing Infrasound Amplitude Determination Results: for all windows the P-P measure provides the correct amplitude in the ‘unfiltered’ case the HFT248D window provides the best analytic amplitude in the unfiltered case. all windows provided reduced amplitudes for a 2-octave band-pass filter centred on the dominant frequency. high frequency resolution general purpose high amplitude resolution

Infrasound Technology Workshop, Bermuda November 2008 Page 23 Infrasound Amplitude Determination Still to be done  evaluate results  compare with analyst measured amplitudes  investigate utility of different amplitude measures  Analytic amplitude as a microbarom classifier ?  the RMS amplitude as a signal significance measure ?  incorporate as part of mainstream IDC processing

Infrasound Technology Workshop, Bermuda November 2008 Page 24  Two enhancements to the automatic infrasound processing system are currently under development:  station noise characterization  amplitude determination  Should be operational in around 3-4 months. Conclusions Summary