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