higher-level clients to leverage mustang metrics dr. mary templeton iris data management center...
TRANSCRIPT
Higher-Level Clients to Leverage MUSTANG Metrics
Higher-Level Clients to Leverage MUSTANG Metrics
Dr. Mary Templeton
IRIS Data Management CenterManaging Data from Seismic Networks
September 9-17 2015
Hanoi, Vietnam
Dr. Mary Templeton
IRIS Data Management CenterManaging Data from Seismic Networks
September 9-17 2015
Hanoi, Vietnam
Why Have Multiple Clients?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
Customizing Quality AssuranceCustomizing 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
Quality Assurance Practice at IRIS DMCQuality 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
Quality Assurance Practice at IRIS DMCQuality Assurance Practice at IRIS DMC
Analyst review Metrics: dead_channel_exp < 0.3 and pct_below_nlnm > 20 Review plot using MUSTANG noise-pdf service
Analyst review Metrics: dead_channel_exp < 0.3 and pct_below_nlnm > 20 Review plot using MUSTANG noise-pdf service
Nepal Earthquake
microseisms
*IU.WCI.00.BHZ isn’t completely dead – it still records some energy
Quality Assurance Practice at IRIS DMCQuality 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
Quality Assurance Practice at IRIS DMCQuality Assurance Practice at IRIS DMC
Analyst review Review sample_mean plot using MUSTANG databrowser
Analyst review Review sample_mean plot using MUSTANG databrowser
Quality Assurance Practice at IRIS DMCQuality Assurance Practice at IRIS DMC
Analyst review Example: Channel Orientation Analysis
The orientation_check metric finds observed channel orientations for shallow M>= 7 events byCalculating the Hilbert transform of the Z component (H{Z}) for Rayleigh wavesCross-correlating H{Z} with trial radial components calculated at varying azimuths until the correlation coefficient is maximizedThe 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 byCalculating the Hilbert transform of the Z component (H{Z}) for Rayleigh wavesCross-correlating H{Z} with trial radial components calculated at varying azimuths until the correlation coefficient is maximizedThe 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.
Quality Assurance Practice at IRIS DMCQuality 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 > 0.4Median 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.
Why Have Multiple Clients?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
Quality Assurance Practice at IRIS DMCQuality Assurance Practice at IRIS DMC
Tracking
Problems
Tracking
Problems
Quality Assurance Practice at IRIS DMCQuality Assurance Practice at IRIS DMC
HTML report
Tracking
Problems
Tracking
Problems
Quality Assurance Practice at IRIS DMCQuality Assurance Practice at IRIS DMC
…
Reporting
Problems
Reporting
Problems
Virtual network report summarized by network
Links to analyst assessment of issue
Strategies for leveraging MUSTANG metrics
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
Strategies for leveraging MUSTANG metrics
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'
Strategies for leveraging MUSTANG metrics
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
A Note About Amplitude MetricsA 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
A Note About Amplitude MetricsA 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
A Note About Amplitude MetricsA Note About Amplitude Metrics
PDF – a “heat-density” plot of many Power Spectral Density curves:
PDF – a “heat-density” plot of many Power Spectral Density curves:
Healthy PSDs
Calibration
Dead channel
New High Noise ModelNHNM
New Low Noise ModelNLNM
Metrics Threshold Example Problem
Metrics Threshold Example Problem
HHE poles:
HHN poles:
Sign error
Strategies for leveraging MUSTANG metrics
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)
Combined MetricsExample Problem
Combined MetricsExample Problem
dead_channel_exp < 0.3 && pct_below_nlnm > 20dead_channel_exp < 0.3 && pct_below_nlnm > 20
Strategies for leveraging MUSTANG metrics
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
Metrics Arithmetic Example Problem
Metrics Arithmetic Example Problem
A nonzero gap average for all channels with no high num_gap days may indicate an ongoing telemetry problem.
Strategies for leveraging MUSTANG metrics
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 < 0.3 && pct_below_nlnm > 20 pegged: abs(sample_rms) > 10e+7 lowAmp: dead_channel_exp >= 0.3 && pct_below_nlnm > 20 noise: dead_channel_exp < 0.3 && pct_above_nhnm > 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 < 0.3 && pct_below_nlnm > 20 pegged: abs(sample_rms) > 10e+7 lowAmp: dead_channel_exp >= 0.3 && pct_below_nlnm > 20 noise: dead_channel_exp < 0.3 && pct_above_nhnm > 20 hiAmp: sample_rms > 50000 avgSpikes: average spikes/measurement >= 100 dcOffsets: dc_offset > 50 badRESP: pct_above_nhnm > 90 || pct_below_nlnm > 90
Strategies for leveraging MUSTANG metrics
Strategies for leveraging MUSTANG metrics
Scripting your own client can take advantage of these strategies:
Scripting your own client can take advantage of these strategies:
Strategies for leveraging MUSTANG metrics
Strategies for leveraging MUSTANG metrics
Incorporate graphics Incorporate graphics
IRIS DMC QA WebsiteIRIS 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
Thank youThank you