1. Network Interactions with MUSTANG: MUSTANG Clients and Network Reports
Dr. Mary Templeton
IRIS Data Management Center
Managing Data from Seismic Networks
August 20-26, 2017
Pretoria, South Africa

2. Network Interactions
How you can QA your own network using MUSTANG clients
LASSO
MUSTANG Databrowser
How IRIS staff QA networks
Writing custom scripts for metrics retrieval
Verifying and reporting data problems
Tracking problem resolution
Clients retrieve metrics from MUSTANG and present them to you

3. LASSO: A MUSTANG Client
Displays MUSTANG metrics
In control-panel format
As time series
Ranks channels based on metrics values
Can display multiple metrics for virtual networks
Includes some derived metrics
Useful for getting an overview of network health over some time period

4. LASSO: Basic Retrieval
Virtual networks only
Choose from 5 combinations
of measured or derived
metrics describing:
Mass position
Noise power
Signal quality
Time series integrity
Metadata validity
Quantitative ranks channels
From 0 (all metrics poor) to
100 (all metrics good).
Qualitative lists a count of
good, fair and poor metrics.
Basic (pre-select metrics combinations for a full network) and Advanced (you choose your metrics and stations) querying
Snapshot retrieves a single day; mean and median retrieve longer time spans

5. LASSO Control Panel View
Click column
name to sort
Qualitative ranking:
2 good
1 fair
0 poor
Note: m1-3 are derived metrics that scale the mean amplitude of a mass position channel in V/Count and scales it to Volts (the value shown here)
The color is determined by mass position volts divided by a sensor type-dependent voltage threshold as described by “m3 Rules”

6. LASSO: Advanced Retrieval
You choose metrics combinations
Network or Virtual Network code
You choose the metrics, stations and channels that you’re interested in

7. Completeness Metrics Metrics combination Table type Start/End times
max_gap
num_gaps
percent_availability
Table type
Mean for Period
Start/End times to
One metrics combination that I find useful

8. IU Completeness Metrics
Data stopped flowing on
2017/07/07
Click cell to graph values:
Outage began with a single “gap”
We asked for mean metric values over a week. Plots will reveal the daily details

9. Other Metrics Combinations
Amplitude health
dead_channel_exp – seismic signal absent
low values suspect
pct_below_nlnm – low amplitude levels
high values suspect
pct_above_nhnm – noise
non-zero values suspect
sample_mean – mass drift
very high values suspect
sample_rms – amplitude variation
very low or very high values suspect
Here’s another metric combo that I find useful

10. Other Metrics Combinations
Signal anomalies
cross_talk – same signal on multiple channels
Absolute values near 1 suspect
dc_offset – step difference in sample_mean
High values suspect
num_spikes – transients lasting one to a few samples
sample_unique – count of unique amplitude values
Very high or very low values suspect
And a third combo

11. MUSTANG Databrowser: Another Client
Displays MUSTANG metrics
As network or station “boxplots”
As time series
Grouped with similar channels
Grouped with related metrics
Displays seismic traces
Displays PDF plots
Good for exploring the full story (multiple metrics) behind a single suspicious metric value

12. Exploring Amplitude Health with Boxplots
IU.BILL.00.BHZ has unusually high mean amplitudes!
The dark blue or “boxed” area shows where 50% of the values lie
Except for “outliers” all values fall between the “whiskers”

13. Exploring Amplitude Health
Seismic trace: drift is confirmed
Metric Timeseries:
Drift began April 8, 2017
PDF Plot: Seismic energy is absent

14. How IRIS staff QA networks
IRIS staff used to review waveforms to QA some special networks – staff intensive
We wanted to develop an automated metrics-based approach – less staff intensive
Special thanks to the Global Seismic Network and the Alaska Regional Network for their feedback as we tested this approach using their data
We’ve assumed you’ve already registered your network and station names.
We will do the next three steps before lunch.
After lunch, we’ll do the last step.

15. How IRIS staff QA networks
Custom R scripts retrieve metrics:
Metric thresholds suggesting problem data
Unlike the interactive clients we seen, we created R scripts so they could be automated
We’ve typically QAed a month of network data for each report

16. How IRIS staff QA networks
Verify and report data problems
An analyst still needs to verify that “suspicious-looking metric values” are a true data problem
The previous problem began on November 19 and was still occurring on the report date of December 20

17. How IRIS staff QA networks
Track Problem Resolution
True data problems are recorded in a tracking system.
When the problem is resolved, the end data is entered and the issue is marked “Closed”
Closed problems are typically no longer reported to network operators.

18. Can Anyone Write Custom Clients?
Yes!!!
Here are two tutorials to help you learn more:
Seismic Data Quality Assurance Using IRIS MUSTANG Metrics
R Class for Seismologists
Clients can be written in any language that can send HTTP queries, often with the curl or wget command