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. PSD Probability Density Function for ISCO BHZ
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.

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. Artifacts in the Noise Field
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.

6. Network SOH monitoring Dead station Detection Modeling Design Planning
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
Realistic view of noise conditions at a station. Not simply lowest levels experienced.
McNamara and Buland (2004) BSSA

7. Network SOH monitoring Dead station Detection Modeling Design Planning
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. 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
Lightning strike hours after
Station began operation

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. Mw
PVMO

11. Detection Maps Used for Prioritization of Maintenance Issues
Backbone Stations on Satellite GR4
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. Mw
Backbone stations on Satellite SM5

21. Network SOH monitoring Dead station Detection Modeling Design Planning
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
3km from train
20km from train
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. Network SOH monitoring Dead station Detection Modeling Design Planning
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. Network SOH monitoring Dead station Detection Modeling Design Planning
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 Constructed from 90th percentile computed from
PDFs binned for
each hour of the
day.
Data from
Sept 2001 to
Oct 2004
6am local time
Noise across all periods increases 10-15dB during the working day
with the exception of the microseism band (~7-8s).

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

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