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

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.

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.

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

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.

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 08/2001-05/2002 Realistic view of noise conditions at a station. Not simply lowest levels experienced. McNamara and Buland (2004) BSSA

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 http://gldqc/cgi-bin/pdf IRIS DMC http://www.iris.washington.edu/servlet/quackquery/

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

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

Regional Network Simulation 6 stations from NM regional network with well established noise baselines. Detection threshold lowered in New Madrid region by 0.1-0.3 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

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

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.

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.

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.

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

Hurricanes

Seismometer casing differential motion

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

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

Regional Noise Characteristics

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

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).

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.

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

Noise Baselines: Which Statistic? Mode, Average or Median

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