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Power Spectral Density (PSD) Probability Density Functions (PDF) Seismic Data QC, Network Design Tool and Capability Modeling Developers: Dan McNamara,

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Presentation on theme: "Power Spectral Density (PSD) Probability Density Functions (PDF) Seismic Data QC, Network Design Tool and Capability Modeling Developers: Dan McNamara,"— Presentation transcript:

1 Power Spectral Density (PSD) Probability Density Functions (PDF) Seismic Data QC, Network Design Tool and Capability Modeling Developers: Dan McNamara, Ray ANSS NOC Richard Boaz Consultancy Others involved: Harold Bolton, Jerry ANSS IDCC Paul Earle, Harley Benz, Rob ANSS NEIC Tim Ahern, Bruce IRIS DMC

2 Individual histograms for each period are converted to PDFs by normalizing each power bin by total number of observations. Total distribution of powers plotted. Not simply minimum powers. PSD Probability Density Function for ISCO BHZ

3 Method: Power Spectral Density Probability Density Functions Raw waveforms continuously extracted from waveserver In 1 hour segments, overlapping by 50%. PSD pre-processing: trend and mean removal 10% cos taper applied No screening for earthquakes, or transients and instrumental glitches such as data gaps, clipping, spikes, mass re-centers or calibration pulses PSD calculated for each 1 hour segment With ASL algorithm for direct comparison to NLNM. PSD is smoothed by averaging powers over full octaves in 1/8 octave intervals. Points reduced from 16,385 to 93. Center points of octave averages shown.

4 Power Frequency Distribution Histograms PSDs are accumulated in 1dB power bins from -200 to -50dB. Distributions are generated for each period in 1/8 octave period intervals. Histograms vary significantly by period. - 1s has strong peak and a narrow range of powers. - bimodal distributions at 10, 100s -All have sharp low-power floor with higher power tails Next step: Convert histograms to Probability Density Functions

5 HLID - automobile traffic along a dirt road only 20 meters from station HLID creates a 20-30dB increase in power at about 0.1 sec period (10Hz). This type of cultural noise is observable in the PDFs as a region of low probability at high frequencies (1-10Hz, 0.1-1s). Body waves occur as low probabily signal in the 1sec range while surface waves are generally higher power at longer periods. Automatic mass re-centering and calibration pulses show up as low probability occurrences in the PDF. Artifacts in the Noise Field

6 Current Noise PDF Uses Network SOH monitoring Dead station Detection Modeling Design Planning Station Quality Site quality Current stations future backbone ANSS Rankings Noise Research sources hurricanes ambient noise model Hailey, ID 08/ /2002 McNamara and Buland (2004) BSSA Realistic view of noise conditions at a station. Not simply lowest levels experienced.

7 Current Noise PDF Uses Network SOH monitoring Dead station Detection Modeling Design Planning Station Quality Site quality Current stations future backbone ANSS Rankings Noise Research sources hurricanes ambient noise model GOTO: ANSS QC IRIS DMC

8 Lightning strike hours after Station began operation Current Noise PDF Uses Network SOH monitoring Dead station Detection Modeling Design Planning Station Quality Site quality Current stations future backbone ANSS Rankings Noise Research sources hurricanes ambient noise model

9 Current Noise PDF Uses Network SOH monitoring Dead station Detection Modeling Design Planning Station Quality Site quality Current stations future backbone ANSS Rankings Noise Research sources hurricanes ambient noise model Brune minimum Mw Mw

10 Regional Network Simulation 6 stations from NM regional network with well established noise baselines. Detection threshold lowered in New Madrid region by units with addition of NM network. Regional Station Limitations: - high noise in Cultural noise band (1-10Hz) - PVMO instrumented with Guralp CMG- 3esp seismometer (50Hz) and Quanterra Q- 380 digitizer at 20sps. Power rolloff at Nyquist~10Hz. PVMO Mw

11 Backbone Stations on Satellite GR4 Backbone stations on Satellite SM5 Detection Maps Used for Prioritization of Maintenance Issues Mw ANSS backbone distributed over 2 satellites to protect against total network outage. Simulations demonstrate detection in the event of a satellite failure. Maintenance decisions could be made based on real-time changes in detection thresholds. GR4 expected to die within 3 years.

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21 3km from train 20km from train Current Noise PDF Uses Network SOH monitoring Dead station Detection Modeling Design Planning Station Quality Site quality Current stations future backbone ANSS Rankings Noise Research sources hurricanes ambient noise model Meremonte, M., D. McNamara, A. Leeds, D. Overturf, J. McMillian, and J. Allen, ANSS backbone station installation and site characterization, EOS Trans. AGU, 85(47), 2004.

22 Current Noise PDF Uses Network SOH monitoring Dead station Detection Modeling Design Planning Station Quality Site quality Current station future backbone ANSS Rankings Noise Research sources hurricanes ambient noise model GSN Standing Committee Report : An Assessment of Seismic Noise Characteristics for the ANSS Backbone and Selected Regional Broadband Stations By D. McNamara, Harley M. Benz and W. Leith Also McNamara, D.E., H.M. Benz and W. Leith, USGS Open-File Report, in press, 2005.

23 Current Noise PDF Uses Network SOH monitoring Dead station Detection Modeling Design Planning Station Quality Site quality Current station future backbone ANSS Rankings Noise Research sources hurricanes ambient noise models McNamara, D.E., R.P. Buland, R.I. Boaz, B. Weertman, and T. Ahern, Ambient seismic noise, Seis. Res. Lett., in press, 2005.

24 Plans for future development: QDAT Database hourly PSDs to allow: creative selection of data for PDF generation Playback as a movie (i.e. graphic equalizer) Additional types of visualizations Regional noise trends diurnal and seasonal variations Research noise sources baselines auto ID of problem artifacts Operations vault design telemetry performance automated problem reporting and notification

25 Hurricanes

26 Seismometer casing differential motion

27 Plans for future development: QDAT Database hourly PSDs to allow: creative selection of data for PDF generation Playback as a movie (i.e. graphic equalizer) Additional types of visualizations Regional noise trends diurnal and seasonal variations spectograms Research noise sources baselines auto ID of problem artifacts Operations vault design telemetry performance automated problem reporting and notification

28 Plans for future development: QDAT Database hourly PSDs to allow: creative selection of data for PDF generation Playback as a movie (i.e. graphic equalizer) Additional types of visualizations Regional noise trends diurnal and seasonal variations spectograms Research noise sources baselines auto ID of problem artifacts Operations vault design telemetry performance automated problem reporting and notification

29 Regional Noise Characteristics

30 Plans for future development: QDAT Database hourly PSDs to allow: creative selection of data for PDF generation Playback as a movie (i.e. graphic equalizer) Additional types of visualizations Regional noise trends diurnal and seasonal variations spectograms Research noise sources baselines auto ID of problem artifacts Operations vault design telemetry performance automated problem reporting and notification

31 6am local time Noise across all periods increases 10-15dB during the working day with the exception of the microseism band (~7-8s). Constructed from 90th percentile computed from PDFs binned for each hour of the day. Data from Sept 2001 to Oct 2004

32 Short period noise increases during the summer months. Microseism band (~7-8s) noise increases during the fall and winter. Constructed from 90th percentile computed from PDFs binned for each month of the year. Data from Sept 2001 to Oct 2004 School begins

33 Plans for future development: QDAT Database hourly PSDs to allow: creative selection of data for PDF generation Playback as a movie (i.e. graphic equalizer) Additional types of visualizations Regional noise trends diurnal and seasonal variations spectograms Research noise sources baselines auto ID of problem artifacts Operations vault design telemetry performance automated problem reporting and notification

34 Noise Baselines: Which Statistic? Mode, Average or Median

35 Plans for future development: QDAT Database hourly PSDs to allow: creative selection of data for PDF generation Playback as a movie (i.e. graphic equalizer) Additional types of visualizations Regional noise trends diurnal and seasonal variations spectragrams Research noise sources baselines auto ID of problem artifacts Operations vault design telemetry performance automated problem reporting and notification


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