A New QUACK Controlet: Sesimic Noise Probability Density Functions Developers: Dan McNamara, Ray Buland, Harold Bolton, Jerry USGS Richard Boaz.

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Presentation transcript:

A New QUACK Controlet: Sesimic Noise Probability Density Functions Developers: Dan McNamara, Ray Buland, Harold Bolton, Jerry USGS Richard Boaz Consultancy (formerly IRIS PASSCAL) Tim Ahern, Bruce IRIS DMC

IRIS-USGS Collaborative QC Development Effort USGS noise PDF algorithm - Summer 2001 USGS informal QC PDF - Fall 2001 IRIS DMC Quack RFP - Fall 2002 Proposal funded - Spring 2003 Programmer hired - Fall 2003 DMC QUACK PDF operational - June 2004 BSSA Paper published - Aug USGS Formal QC PDF - Sept Stand Alone PDF -Oct. 2004

PDFs at IRIS DMC QUACK interface USarray data PDF Methods and Explanation website

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. -some periods (1s) have strong peak and a narrow range of powers. -others have double peaks (10,100s) -All have sharp low-power floor with higher power tails Next step: Convert histograms to Probability Density Functions

Individual histograms for each period are converted to PDFs by normalizing each power bin by total number of observations and combined into one color plot. Probability Density Function for ISCO BHZ Establish noise baselines using mode.

ANSS Seismic Noise Monitoring Establish ANSS station noise baselines Select ANSS backbone instrumentation ANSS backbone site criteria Network detection thresholds Station maintenance issues –System transients –Prioritize repairs –Automate problem notification Cultural noise source modeling Microseism modeling Motivation Hailey, ID 08/ /2002 All data is included, no pre-screening for quakes, data gaps, glitches, high noise data individual PSDs, binned in 1/8 octave intervals, are used to construct a Seismic Noise Probability Density Function for HLID BHZ. McNamara and Buland (2004) BSSA Cars Local Quakes Teleseisms Approach Results Realistic view of noise conditions at a station. Not simply lowest levels experienced.

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

Current Noise PDF Uses Network monitoring Instrument response Dead station Detection Modeling Station Quality Current stations future backbone ANSS Rankings Noise Research sources hurricanes ambient noise model Lightning strike hours after field crew left site

Current Noise PDF Uses Network monitoring Station Quality Instrument response Dead station Detection Modeling Station Quality Current stations future backbone ANSS Rankings Noise Research sources hurricanes ambient noise model Brune minimum Mw, 80% noise threshold Mw

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

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. Hughes states. “There will be a seamless transition to a new satellite…”

Current Noise PDF Uses Network monitoring Instrument response Dead station Detection Modeling Station Quality Current stations future backbone ANSS Rankings Noise Research sources hurricanes ambient noise model 3km from train 20km from train

Plans for future development 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 auto ID of problem artifacts Operations vault design telemetry performance automated problem reporting and notification

Hurricanes

sts2 automatic mass re-centers

Plans for future development 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 auto ID of problem artifacts Operations vault design telemetry performance automated problem reporting and notification

Regional Noise Characteristics

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.

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. School begins

Plans for future development 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 auto ID of problem artifacts Operations vault design and station siting telemetry performance automated problem reporting/notification

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 PDFs at IRIS DMC QUACK interface