Workshop on Transient Anomalous Strain Detection

1. 2011 SCEC Annual Meeting: Workshop on Transient Anomalous Strain Detection Jessica Murray-Moraleda, U. S. Geological Survey Rowena Lohman, Cornell University September 11, 2011

2. From the SCEC3 Science Objectives: “Develop a geodetic network processing system that will detect anomalous strain transients” • Systematic monitoring lagged despite • Growth in permanent GPS and strainmeter networks • InSAR time series analysis techniques • Growing number of transient events observed world-wide SCEC CMM 4.0

3. From the SCEC3 Science Objectives: “Develop a geodetic network processing system that will detect anomalous strain transients” • Transient detection algorithms enable • Real-time monitoring of transient deformation and associated seismicity • Characterization of signals for investigating underlying processes • Identification of non-tectonic signals • Tracking of data quality • Planning future network development to improve detection thresholds. SCEC CMM 4.0

4. Key issues: 2008-2011 • What is a transient? • Real-time monitoring of transient deformation and associated seismicity • Characterization of signals for investigating underlying processes • Identification of non-tectonic signals • Tracking of data quality • Planning future network development to improve detection thresholds. Previous efforts: • Require some spatial, temporal coherence • “Characterization” requires treatment of seasonal + new, larger postseismic • Retrospective vs. real-time analysis • Discuss today • Target audience? • Make progress before AGU

5. Cascadia slow slip event recorded in east component at ALBH 15 days 5 mm Dragert et al. (2001) Transients vary in duration and amplitude Slow slip event in Manawatu region of the North Island of New Zealand 2.5 cm 7 mos. Wallace and Beavan (2006)

6. Transients often propagate spatially The signal may only be apparent on a small number of sites at any given time. Space-time history of Cascadia slow slip events Szeliga et al., 2008)

7. Bennett (2008) King et al. (2007) 1996 1998 2000 2002 2004 2006 Data are contaminated by time-varying non-tectonic signals Above right: time-varying seasonal signal; Below: spatially-coherent hydrologic signal

8. Apparent transients can signal site-specific problems Above: seasonal trend actually due to a malfunctioning antenna; Left: apparent transient due to snow on the antenna. Figures courtesy of Tom Herring (MIT)

9. August 2008 • Held a brainstorming workshop. • Framed the problem. • Identified test exercise as preferred approach to • foster an active community of researchers • explore promising methodology • combine effective approaches in novel ways. • Debated use of real versus synthetic data.

10. Workshop September 2008 Announced Transient Detection Exercise at SCEC Annual Meeting.

11. Exercise announced Workshop • January 2009 • Began Phase I. • Established group websites for file exchange (data, results, true signals) and discussion. http://www.scec.org/research/projects/TransientDetection/ http://groups.google.com/group/SCECtransient

12. Phase I begins Exercise announced Workshop • March 2009 • Phase I results submitted. • High SNR case • Primarily used for validating code

13. Phase I begins Phase I ends Exercise announced Workshop June 2009 Phase II data released.

14. Phase II begins Phase I begins Phase I ends Exercise announced Workshop • August 2009 • Phase II results submitted. • All high SNR signals were detected and well-characterized. • Low SNR signals were almost universally undetected.

15. Phase II begins Phase I begins Phase II ends Phase I ends Exercise announced Workshop September 2009 Workshop held at the SCEC annual meeting.

16. Phase II begins Phase I begins Phase II ends Phase I ends Exercise announced Workshop Workshop October 2009 Phase IIC data released.

17. Phase IIC begins Phase II begins Phase I begins Phase II ends Phase I ends Exercise announced Workshop Workshop December 2009 Convened special session “Detection and Characterization of Transient Crustal Deformation” at Fall AGU meeting.

18. Phase IIC begins Phase II begins Phase I begins Phase II ends Phase I ends AGU session Exercise announced Workshop Workshop February 2010 Phase IIC results submitted.

19. Phase IIC begins Phase III begins Phase II begins Phase IIC ends Phase I begins Phase II ends Phase I ends AGU session Exercise announced Workshop Workshop August 2010 Phase III results submitted.

20. Phase IIC begins Phase III begins Phase II begins Phase IIC ends Phase I begins Phase II ends Phase III ends Phase I ends AGU session Exercise announced Workshop Workshop September 2010 workshop

21. Phase IIC begins Phase III begins Phase II begins Phase IIC ends Phase I begins Phase II ends Phase III ends Phase I ends AGU session Exercise announced Workshop Workshop Workshop December 2010 AGU special session “Development and Testing of Methods for Detecting and Estimating Unsteady Motion in Geodetic Time Series” in conjunction with Simon Williams.

22. Phase IIC begins Phase III begins Phase II begins Phase IIC ends Phase I begins Phase II ends Phase III ends Phase I ends AGU session AGU session Exercise announced Workshop Workshop Workshop Phase IV data released and examined by subset of groups

23. Phase IIC begins Phase III begins Phase II begins Phase IIC ends Phase I begins Phase II ends Phase III ends Phase I ends AGU session AGU session Exercise announced Workshop Workshop Workshop 2013 2012 Maria Liukus implements Lohman test algorithm in testing center

24. Phase IIC begins Phase III begins Phase II begins Phase IIC ends Phase I begins Phase II ends Phase III ends Phase I ends AGU session AGU session Exercise announced Workshop Workshop Workshop 2013 2012 Todays workshop

25. Phase IIC begins Phase III begins Phase II begins Phase IIC ends Phase I begins Phase II ends Phase III ends Phase I ends AGU session AGU session Exercise announced Workshop Workshop Workshop Workshop 2013 2012 AGU workshop

26. Outcomes from previous workshops • Results obtained using different algorithms: • Successful at retrospectively detecting signals already visible in time series • Less successful with subtle signals • Real-time capabilities not yet assessed • Need to establish detection thresholds as a function of • signal magnitude • spatial extent • duration • network configuration • Need to quantify the false alarm rate (will be easy once codes are “detection-center-ready) • In Phase III, onwards, participants reported confidence on detections, generally in a qualitative sense.

27. Outcomes from previous workshops • Refinements to be made to synthetic test data: • Data covariance: • The noise spectra of the test data can be assessed using statistical methods without the data covariance, but the covariance provides information about bad data • Simulating the data covariance structure for synthetic data will require further examination of error statistics • More subtle signals • More realistic signals such as • offsets (coseismic or instrumental) • spatially-coherent non-tectonic transients • postseismic (of various mechanisms) • Action: Phase IV workshops had a variety of signals, with seasonal variations that varied from year to year. No postseismic transients yet. Non-tectonic transients included in PhaseIII went mostly undetected.

28. Outcomes from previous workshops • Synthetic versus real test data: • Synthetic data • is useful for trouble-shooting and improving algorithms • makes assessing success easier • embodies assumptions about signals • encourages “tuning” of algorithms to anticipated signals • Consensus: There is substantial additional source complexity yet to be added to the synthetic time series, and algorithms are still early in development, so continue to focus on synthetic test data • Since last year: • Duncan’s code was made freely available • Three new datasets appeared to be examined by only three groups • Barriers?

29. Some thoughts on future directions for transient detection • Who will use detection algorithms? How will they use them? • What is the optimal level of physics that should be brought to bear? Is it enough to identify that a change is taking place? • What range of signal characteristics can one algorithm be expected to detect? To what extent should the algorithm be expected to classify the source? • How should we quantify the level of certainty at which a detection is made? How do these requirements vary depending on user? • For real data - how do we deal with the large, known transients at Parkfield, Mojave and now in the Salton Trough?