Noise Based Detection Method for the ANSS

1. Noise Based Detection Method for the ANSS by Dan McNamara With Collaborators: Ray Buland, Harley Benz, Rob Wesson Art Frankel and Dirk Erickson

2. Topics • ANSS Probabilistic Noise Analysis • Noise Based Detection Technique • Detection System Applications • ANSS Network Design Recommendations

3. Motivation ANSS Seismic Noise Monitoring Hailey, ID 08/2001-05/2002 • Establish ANSS station noise baselines • 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 Approach Cars Local Quakes Teleseisms All data is included, no pre-screening for quakes, data gaps, glitches, high noise data. 2370 individual PSDs, binned in 1/8 octave intervals, are used to construct a Seismic Noise Probability Density Function for HLID BHZ. Results Realistic view of noise conditions at a station. Not simply lowest levels experienced. McNamara and Buland (2004) in press BSSA

4. Seismic Noise PDFsnoise as a function of location and site type Continental Interior: Mine Site Continental Interior: Borehole Idaho Springs, CO Bozeman, MT Western US rocks sites tend to have low noise although the minimum is generally higher than the NLNM.

5. Seismic Noise PDFsnoise as a function of location and site type Eastern US: Surface vault Binghamton, NY Island Site: Borehole Big Island Hawaii Higher noise across all bands in Highly populous Eastern US. Very high noise in microseism band But quiet at long periods due to borehole.

6. Topics • ANSS Backbone Probabilistic Noise Analysis • Noise Based Detection Technique • Brune source modeling method • Comparison of Brune source modeling results with NEIC autopicker • Detection System Applications • ANSS Network Design Recommendations

7. Method to Compute Theoretical ANSS Detection Threshold based on Brune Source Modeling. For each 1 degree cell we model Brune sources over a range of frequencies Brune (1970, 1971). A detection is declared if the Brune source P-wave amplitude exceeds our noise threshold at 5 ANSS stations. Calculations For each frequency (1/period) per cell. Shear-wave moment (dyne-cm) Brune (1970, 1971). fc=10Hz Mw=3.1 fc=1Hz Mw=5.1 Fault Dimension in cm Mw = 0.667 log(Mo) – 10.7 (Kanimori, 1977) Compute shear-wave amplitude from Mw (Brune 1970, 1971). Apply Q(f) models to shear-wave amplitude. Convert to P-wave amplitude. Convert velocity amplitude to dB for noise comparison.

8. Topics • ANSS Backbone Probabilistic Noise Analysis • Noise Based Detection Technique • Brune source modeling method • Comparison of Brune source modeling results with NEIC autopicker • Detection System Applications • ANSS Network Design Recommendations

9. ANSS Detection Threshold Modeling Results Brune minimum Mw, 80% noise threshold Used 63 existing ANSS backbone stations with well established noise baselines. Detection declared if at least 5 stations in solution. Model shows minimum Mw at regions where network is dense in western and eastern US. Mw max occur in regions of low station density. Model minimums ~0.2 units higher than catalog. Model maximums ~0.2 units lower than catalog. General pattern close match. Mw/mb NEIC Autopicker Minimum mb.

10. ANSS Detection Threshold Modeling Results Brune minimum Mw, PDF mode noise threshold Mw/mb PDF mode noise threshold pattern similar to 80th with minimum Mw regions expanded. PDF mode noise threshold demonstrates how lowering noise can extend minimum detection threshold. Model minimums ~0.1 units higher than catalog. Model maximums 1.0-1.2 units lower than catalog. General pattern close match but overall pattern better matched by 80th noise threshold. NEIC Autopicker Minimum mb.

11. Brune source modeling not an exact match to NEIC autopicker? • Mb:Mw bias? • Simplistic application of Q models • Noise baselines affected by system transients • Incomplete and complicated autopicker catalog

12. mb:Mw Bias UC Berkeley Northern CA Moment Tensor Catalog 1988-2004 For mb 5.5-7.3 Mw=1.46mb-2.42 Sipkin (2003) No Magnitude bias at small mb

13. Brune source modeling not an exact match to NEIC autopicker? • Mb:Mw bias? • Simplistic application of Q models • Noise baselines affected by system transients • Incomplete and complicated autopicker catalog

14. New frequency Dependent Q Models 3Hz Q Considerable time spent modeling Lg amplitudes for frequency dependent US Q. Erickson et al, 2004; McNamara et al 2004; Wesson and McNamara 2003. At this point Q(f) chosen by source region. More realistic approach is to project each path through Q model to more accurately predict amplitudes. Should lead to better modeling of Mw regional variations. 6Hz

15. Brune source modeling not an exact match to NEIC autopicker? • Mb:Mw bias? • Simplistic application of Q models • Noise baselines affected by system transients • Incomplete and complicated autopicker catalog

16. System Transients can have an effect on noise PDF levels. 90th percentile and mode often track data dropouts when frequent. Causing localized detection anomalies. Data Dropouts

17. Brune source modeling not an exact match to NEIC autopicker? • Mb:Mw bias? • Simplistic application of Q models • Noise baselines affected by system transients • Incomplete and complicated autopicker catalog

18. NEIC Minimum Auto Detection Catalog Issues mb Catalog possibly incomplete (only 20 months in 2002-2003) Possible false triggers at mb minimums. Mine blasts that do not behave like earthquakes at mb minimums. Multiple magnitude types (mb, ml, mbLg) Therefore, difficult to achieve exact match.

19. Brune source modeling not an exact match to NEIC autopicker? • Mb:Mw bias? • Simplistic application of Q models • Noise baselines affected by system transients • Incomplete and complicated autopicker catalog Match good enough to play games with detections and learn some things about the network!

20. Topics • ANSS Backbone Probabilistic Noise Analysis • Noise Based Detection Technique • Detection System Applications • Regional Network Evaluation • Maintenance Prioritization • ANSS Network Design • ANSS Network Design Recommendations

21. 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

22. Topics • ANSS Backbone Probabilistic Noise Analysis • Noise Based Detection Technique • Detection System Applications • Regional Network Evaluation • Maintenance Prioritization • ANSS Network Design • ANSS Network Design Recommendations

23. Detection Maps Used for Prioritization of Maintenance Issues Satellite GR4 ANSS backbone distributed over 2 satellites to protect against total network outage. 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…” Mw Satellite SM5

24. Topics • ANSS Backbone Probabilistic Noise Analysis • Noise Based Detection Technique • Detection System Applications • Regional Network Evaluation • Maintenance Prioritization • ANSS Network Design • ANSS Network Design Recommendations

25. ANSS Site Location Planning SNSD PDF noise baselines used to estimate noise characteristics in regions without existing ANSS stations. Interpolate from nearby stations with known noise baselines. With noise baseline estimates we can calculate detection thresholds for new network configurations. SNSD

26. ANSS Site Location Planning Mw 22 planned ANSS backbone stations added to simulate future detection capabilities. Mw threshold lowered in regions with sparse station coverage such as the northern midwest and Texas.

27. Topics • ANSS Backbone Probabilistic Noise Analysis • Noise Based Detection Technique • Detection System Applications • Regional Network Evaluation • Maintenance Prioritization • ANSS Network Design • ANSS Network Design Recommendations • Lower station noise thresholds • Supplement backbone with regional stations • Install Planned ANSS stations • Recording system limitations

28. ANSS Network Design Recommendations Based on detection work, we can lower detection thresholds across US. 80th Percentile Noise Level, Brune Mw Mw Decrease Station Noise Levels Supplement with Regional Broadbands Install Planned ANSS Stations Minimum saturation occurs at Mw~2.2-2.5 despite network improvements.

29. Topics • ANSS Backbone Probabilistic Noise Analysis • Noise Based Detection Technique • Detection System Applications • Regional Network Evaluation • Maintenance Prioritization • ANSS Network Design • ANSS Network Design Recommendations • Lower station noise thresholds • Supplement backbone with regional stations • Install Planned ANSS stations • Recording system limitations

30. NEIC Short Period Filter Limitations Higher frequencies required to record full amplitudes of smaller earthquakes. Recommendations: 1. Get rid of SP filter. 2. Increase sampling rate.

31. Detection Simulation with NEIC Filters Removed Mw Mw Thresholds lowered significantly across US with the removal of NEIC Short period filter and sampling rate increased to 200 sps. Noise levels projected to higher frequencies. At 200sps fny~100Hz Mw=2.0 fc=35Hz Mw=1.5 fc=62Hz Mw=1.0 fc=111Hz Difficulties: Short period filters reduce false triggers. New picker would need filters to deal with false triggers while allowing high frequencies through for small events.

32. Conclusions • Detection System Useful for Several Applications • Regional Network Evaluation • Maintenance Prioritization • ANSS Network Design • ANSS Network Design Recommendations • Lower station noise thresholds • Supplement backbone with regional stations • Install Planned ANSS stations • Record higher frequencies