1. Higher-Level Clients to Leverage MUSTANG Metrics Dr. Mary Templeton IRIS Data Management Center Managing Data from Seismic Networks September 9-17 2015 Hanoi, Vietnam Dr. Mary Templeton IRIS Data Management Center Managing Data from Seismic Networks September 9-17 2015 Hanoi, Vietnam

2. Why Have Multiple Clients? Quality Assurance Practice at IRIS DMC Finding problems Analyst review Tracking problems Reporting problems Quality Assurance Practice at IRIS DMC Finding problems Analyst review Tracking problems Reporting problems

3. Customizing Quality Assurance Strategies for leveraging MUSTANG metrics Scripting your own clients wget curl R Strategies for leveraging MUSTANG metrics Scripting your own clients wget curl R

4. Quality Assurance Practice at IRIS DMC Finding Problems: Automated Text Reports (internal use) A script retrieves MUSTANG metrics Metrics are grouped by problem type Focuses on problem stations for further review Finding Problems: Automated Text Reports (internal use) A script retrieves MUSTANG metrics Metrics are grouped by problem type Focuses on problem stations for further review

5. Quality Assurance Practice at IRIS DMC Analyst review Metrics: dead_channel_exp 20 Review plot using MUSTANG noise-pdf service Analyst review Metrics: dead_channel_exp 20 Review plot using MUSTANG noise-pdf service Nepal Earthquake microseisms *IU.WCI.00.BHZ isn’t completely dead – it still records some energy

6. Quality Assurance Practice at IRIS DMC Analyst review Review plot using MUSTANG noise-mode-timeseries service Analyst review Review plot using MUSTANG noise-mode-timeseries service Problem started on August 27 2014

7. Quality Assurance Practice at IRIS DMC Analyst review Review sample_mean plot using MUSTANG databrowser Analyst review Review sample_mean plot using MUSTANG databrowser

8. Quality Assurance Practice at IRIS DMC Analyst review Example: Channel Orientation Analysis The orientation_check metric finds observed channel orientations for shallow M>= 7 events by Calculating the Hilbert transform of the Z component (H{Z}) for Rayleigh waves Cross-correlating H{Z} with trial radial components calculated at varying azimuths until the correlation coefficient is maximized The observed channel orientation is difference between the calculated event back azimuth and observed radial azimuth Analyst review Example: Channel Orientation Analysis The orientation_check metric finds observed channel orientations for shallow M>= 7 events by Calculating the Hilbert transform of the Z component (H{Z}) for Rayleigh waves Cross-correlating H{Z} with trial radial components calculated at varying azimuths until the correlation coefficient is maximized The observed channel orientation is difference between the calculated event back azimuth and observed radial azimuth Stachnik, J.C., Sheehan, A.F., Zietlow, D.W., Yang, Z, Collins, J. and Ferris, A, 2012, Determination of New Zealand Ocean Bottom Seismometer Orientation via Rayleigh- Wave Polarization, Seismological Research Letters, v. 83, no. 4, p 704-712.

9. Quality Assurance Practice at IRIS DMC Analyst review orientation_check measurements from 2013 and 2014 for CU.ANWB having correlation coefficients > 0.4 Analyst review orientation_check measurements from 2013 and 2014 for CU.ANWB having correlation coefficients > 0.4 Median observed Y azimuth differed from the metadata by -2.79 degrees This value was omitted from the median because it fell outside two standard deviations A discrepancy with the CU.TGUH.00 metadata orientation was found using this metric. Its metadata has since been corrected.

10. Why Have Multiple Clients? You can browse small networks by channel: But for large networks, a retrieving a list is faster You can browse small networks by channel: But for large networks, a retrieving a list is faster percent_availability box plot

11. Quality Assurance Practice at IRIS DMC Tracking Problems Tracking Problems

12. Quality Assurance Practice at IRIS DMC HTML report Tracking Problems Tracking Problems

13. Quality Assurance Practice at IRIS DMC … Reporting Problems Reporting Problems Virtual network report summarized by network Links to analyst assessment of issue

14. Strategies for leveraging MUSTANG metrics Use Metrics Thresholds Find problems by retrieving channels that meet a meaningful metrics condition Missing data have percent_availability=0 Channels with masses against the stops have very large absolute_value(sample_mean) Channels that do report GPS locks where clock_locked=0 have lost their GPS time reference Use Metrics Thresholds Find problems by retrieving channels that meet a meaningful metrics condition Missing data have percent_availability=0 Channels with masses against the stops have very large absolute_value(sample_mean) Channels that do report GPS locks where clock_locked=0 have lost their GPS time reference

15. Strategies for leveraging MUSTANG metrics Finding Metrics Thresholds Retrieve measurements for your network wget 'http://service.iris.edu/mustang/measurements/1/ query?metric=sample_mean &net=IU &cha=BH[12ENZ] &format=csv &timewindow=2015-07-07T00:00:00,2015-07-14T00:00:00' Finding Metrics Thresholds Retrieve measurements for your network wget 'http://service.iris.edu/mustang/measurements/1/ query?metric=sample_mean &net=IU &cha=BH[12ENZ] &format=csv &timewindow=2015-07-07T00:00:00,2015-07-14T00:00:00'

16. Strategies for leveraging MUSTANG metrics Finding Metrics Thresholds Find the range of metrics values for problem channels Finding Metrics Thresholds Find the range of metrics values for problem channels Threshold for pegged masses: abs(sample_mean) < 1e+7

17. A Note About Amplitude Metrics Metrics reported in counts may have different thresholds for different instrumentation sample_max sample_mean sample_median sample_min sample_rms Metrics reported in counts may have different thresholds for different instrumentation sample_max sample_mean sample_median sample_min sample_rms

18. A Note About Amplitude Metrics PSD-based metrics have their instrument responses removed – one threshold works for similar (e.g. broadband) instrumentation dead_channel_exp pct_below_nlnm pct_above_nhnm transfer_function PSD-based metrics have their instrument responses removed – one threshold works for similar (e.g. broadband) instrumentation dead_channel_exp pct_below_nlnm pct_above_nhnm transfer_function

19. A Note About Amplitude Metrics PDF – a “heat-density” plot of many Power Spectral Density curves: Healthy PSDs Calibration Dead channel New High Noise Model NHNM New Low Noise Model NLNM

20. Metrics Threshold Example Problem HHE poles: HHN poles: Sign error

21. Strategies for leveraging MUSTANG metrics Combine metrics Dead channels have almost linear PSDs (dead_channel_exp < 0.3) and lie mainly below the NLNM (pct_below_nlnm > 20) Combine metrics Dead channels have almost linear PSDs (dead_channel_exp < 0.3) and lie mainly below the NLNM (pct_below_nlnm > 20)

22. Combined Metrics Example Problem dead_channel_exp 20

23. Strategies for leveraging MUSTANG metrics Metrics Arithmetic Metrics averages num_gaps / # measurements num_spikes / # measurements Metrics differences pct_below_nlnm daily difference Metrics Arithmetic Metrics averages num_gaps / # measurements num_spikes / # measurements Metrics differences pct_below_nlnm daily difference

24. Metrics Arithmetic Example Problem A nonzero gap average for all channels with no high num_gap days may indicate an ongoing telemetry problem.

25. Strategies for leveraging MUSTANG metrics Some favorite metrics tests for GSN data noData: percent_availability = 0 gapsGt12: num_gaps > 12 avgGaps: average gaps/measurement >= 2 noTime: clock_locked = 0 dead: dead_channel_exp 20 pegged: abs(sample_rms) > 10e+7 lowAmp: dead_channel_exp >= 0.3 && pct_below_nlnm > 20 noise: dead_channel_exp 20 hiAmp: sample_rms > 50000 avgSpikes: average spikes/measurement >= 100 dcOffsets: dc_offset > 50 badRESP: pct_above_nhnm > 90 || pct_below_nlnm > 90 Some favorite metrics tests for GSN data noData: percent_availability = 0 gapsGt12: num_gaps > 12 avgGaps: average gaps/measurement >= 2 noTime: clock_locked = 0 dead: dead_channel_exp 20 pegged: abs(sample_rms) > 10e+7 lowAmp: dead_channel_exp >= 0.3 && pct_below_nlnm > 20 noise: dead_channel_exp 20 hiAmp: sample_rms > 50000 avgSpikes: average spikes/measurement >= 100 dcOffsets: dc_offset > 50 badRESP: pct_above_nhnm > 90 || pct_below_nlnm > 90

26. Strategies for leveraging MUSTANG metrics Scripting your own client can take advantage of these strategies:

27. Strategies for leveraging MUSTANG metrics Incorporate graphics

28. IRIS DMC QA Website http://ds.iris.edu/ds/nodes/dmc/quality-assurance/ Currently has links to Existing MUSTANG clients MUSTANG resources and tutorials Interpreting Power Spectral Density graphs We hope to add tutorials on MUSTANG’s R-based metrics packages and other ways to script your own clients in the future http://ds.iris.edu/ds/nodes/dmc/quality-assurance/ Currently has links to Existing MUSTANG clients MUSTANG resources and tutorials Interpreting Power Spectral Density graphs We hope to add tutorials on MUSTANG’s R-based metrics packages and other ways to script your own clients in the future

29. Thank you