1 / 26

Improved Quality Control for Seismic Networks ---MUSTANG

MUSTANG is a system that provides over four dozen quality assurance metrics for seismic networks. It crawls through all data in the archive, not just real-time data, and triggers recalculation of metrics when there are changes in data, metadata, or metric algorithms. It integrates with the IRIS Web Services suite and can be part of a larger network of QA systems. MUSTANG is designed to enhance data quality for domestic and non-US network operators.

edwardb
Download Presentation

Improved Quality Control for Seismic Networks ---MUSTANG

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Improved Quality Control for Seismic Networks ---MUSTANG

  2. Quality Assurance Using MUSTANGModular Utility for Statistical Knowledge Gathering • What is MUSTANG • A system providing ~four-dozen QA metrics • Web services design • Crawls through all data in the archive not just real time data • Changes in data, metadata, metric algorithms trigger recalculation • Integration with IRIS Web Services suite • Metrics available as web services • Can be part of a larger network of QA systems • Metrics derived externally can be incorporated into MUSTANG

  3. IRIS DMC: Enhanced Quality Assurance Domestic & Non-US Network Operators Data Quality Technician PostgreSQL Database MUSTANG Metric Estimators Gaps, overlaps, completeness, signal to noise, power density, pdf mode changes, Glitches, (~ 50 metrics as of July 2014) Archived and Real Time Data

  4. How is MUSTANG designed? • Consists of 3 major components • A Master Scheduler (MCR) • A centralDBMSstorage system (BSS) • A metrics compute cluster mcrmom sched resched jobmgr Node A Node B Node C Node D Node E store

  5. MUSTANG builds upon web services experience • http://service.iris.edu/mustangbeta/

  6. Extensive measurements metrics 43 different metrics

  7. Percent Availability Metric - builder

  8. % Available as a text file or an XML doc or CSV file

  9. MUSTANG builds upon web services experience • http://service.iris.edu/mustangbeta/

  10. Similar for PSDs

  11. MUSTANG builds upon web services experience • http://service.iris.edu/mustangbeta/

  12. Output as XML for programs/computers or text for humans

  13. Or of course as a pictureExample PDF for 1.5 years http://service.iris.edu/mustangbeta/noise-pdf/1/query?net=IU&sta=ANMO&loc=00&cha=BHZ&quality=M&starttime=2013-01-01&endtime=2014-07-10&format=plot&plot.interpolation=bicubic

  14. Visualization Client -LASSO • Visualization client • Provide ability to easily browse metrics data • Provide ability to generate plots of indicated metrics • Provide ability to organize results in web page • Intended audiences • Network operators • Scientific users • http://lasso.iris.edu

  15. LASSO – Advanced interface

  16. LASSO - Traffic Light Display

  17. Visualization Clients that connect with MUSTANG • IRIS Data Services Browser • http://www.iris.edu/mustang/databrowser

  18. MUSTANG Data Browser

  19. Multiple Metric Displays

  20. Network Box Plots

  21. Network Box Plots

  22. Network Box Plots

  23. Station Box Plots

  24. Station Box Plots

  25. New metrics can always be added • The two steps in gain ratio steps can be tracked to metadata changes taking place • This plot represents gain ratios and phase differences • @ T=6s • measured weekly • only values with magnitude squared coherence > 0.999 are shown

  26. IRIS DMC: Research Ready Data Sets Domestic & Non-US Network Operators Data Quality Technician MUSTANG Metric Estimators Gaps, overlaps, completeness, signal to noise, power density, pdf mode changes, Glitches, (~24 metrics in phase 2) PostgreSQL Database DMC Filters Data Request Using Defined Constraints Research Ready Data Sets Archived and Real Time Data Filtered Data Request Returned to Researcher Researcher Specifies Required Data Metric Constraints

More Related