John B Rundle Distinguished Professor, University of California, Davis ( ucdavis ) - PowerPoint PPT Presentation

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John B Rundle Distinguished Professor, University of California, Davis ( ucdavis )
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John B Rundle Distinguished Professor, University of California, Davis ( ucdavis )

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  1. Role of the World Wide Web In Disaster Forecasting, Planning, Management and Response: Challenges and Promise Market Street San Francisco April 14, 1906 YouTube Video John B Rundle Distinguished Professor, University of California, Davis (www.ucdavis.edu) Chairman, Open Hazards Group (www.openhazards.com)

  2. Major Contributors Open Hazards Group: James Holliday (and University of California) William Graves Steven Ward (and University of California) Paul Rundle Daniel Rundle QuakeSim (NASA and Jet Propulsion Laboratory): Andrea Donnellan

  3. On Forecasting • Why forecast? (A vocal minority of our community says we shouldn’t or can’t) • Insurance rates • Safety • Building codes • Fact: Every country in the world has an earthquake forecast (it may be an assumption of zero events, but they all have one) • Premise: Any forecast made by the seismology community is bound to be at least as good as, and probably better than, any forecast made by: • Politicians • Lawyers • Agency bureaucrats

  4. Forecasting vs. Prediction

  5. Challenges in Web-Based Forecasting

  6. California Forecast Features ~5 years in production 250,000 possible rupture rates 37,000 data 1440 branches on logic tree Weights set by expert opinion Constrained to stay close to UCERF2 model, which was constrained by the National Seismic Hazard Map Removes overprediction of M6.7-7 earthquake rates Comprised of 2 fault models; Every additional model requires another 720 logic tree branche

  7. A Different Kind of Forecast: Natural Time Weibull • Features • JBR et al., Physical Review E, 86, 021106 (2012) • J.R. Holliday et al., in review, PAGEOPH, (2014) • A self-consistent global forecast • Displays elastic rebound-type behavior • Gradual increase in probability prior to a large earthquake • Sudden decrease in probability just after a large earthquake • Only about a half dozen parameters (assumptions) in the model whose values are determined from global data • Based on global seismic catalogs • Probabilities are highly time dependent and can change rapidly • Probabilities represent perturbations on the time average probability • Web site displays an ensemble forecast consisting of 20% BASS (ETAS) and 80% NTW forecasts “If a model isn’t simple, its probably wrong” – HirooKanamori (ca. 1980)

  8. NTW MethodJBR et al., Physical Review E, 86, 021106 (2012) • Data from ANSS catalog + other real time feeds • Based on “filling in” the Gutenberg-Richter magnitude-frequency relation • Example: for every ~1000 M>3 earthquakes there is 1 M>6 earthquake • Weibull statistics are used to convert large-earthquake deficit to a probability • Fully automated • Backtested and self-consistent • Updated in real time (at least nightly) • Accounts for statistical correlations of earthquake interactions

  9. NTW-BASS is an Ensemble ForecastJBR et al., Physical Review E, 86, 021106 (2012) J.R. Holliday et al., in review, PAGEOPH, (2014) NTWx 80% Probability = + Probability BASS x 20% Mainshock Probability Time Mainshock “Probability of the earthquake for ETAS is greatest the instant after the earthquake happens” – Ned Field (USGS) Time

  10. Example: Vancouver Island EarthquakesLatest Significant Event was M6.6 on 4/24 /2014JR Holliday et al, in review (2014) Chance of M>6 earthquake in circular region of radius 200 km for next 1 year. Data accessed 4/26/2014 m6.6 11/17/2009 m6.0,6.1 9/3,4/2013

  11. Probability Time Series Sendai, Japan 100 km Radius Accessed 2014/06/25 M8.3 M>7 M8.3 5/24/2013

  12. NTW-BASS is an Ensemble ForecastJBR et al., Physical Review E, 86, 021106 (2012) J.R. Holliday et al., in review, PAGEOPH, (2014) NTWx 80% Probability = + Probability BASS x 20% Probability Time Mainshock “Probability of the earthquake is greatest the instant after it happens” – Ned Field (USGS) Time

  13. Probability Time Series Tokyo, Japan 100 km Radius Accessed 2014/06/25 M8.3 M>7 M8.3 5/24/2013

  14. Probability Time Series Miyazaki, Japan 100 km Radius Accessed 2014/06/25 M8.3 M>6 M8.3 5/24/2013

  15. QuakeWorks Mobile App (iOS)

  16. Verification and Validationhttp://www.cawcr.gov.au/projects/verification/ • Australian site for weather and more general validation and verification of forecasts • Common methods are Reliability/Attributes diagrams, ROC diagrams, Briar Scores, etc.

  17. Optimized 48 month Japan forecast: Probabilities (%) vs. Time for Magnitude ≥ 7.25 & Depth < 40 KM Verification: Example Japan NTW Forecast Assumes Infinite Correlation Length Optimal forecasts via backtesting, with most commonly used verification testing procedures. Forecast Date: 2013/04/10 • Scatter Plot • 1980-present • Observed Frequency vs. Computed Probability Temporal Receiver Operating Characteristic 1980-present

  18. Challenges in Web-Based Forecasting

  19. Use small earthquakes (m ≥ 3.5) to forecast large earthquakes (M ≥ 6). N = # small earthquakes per 1 large earthquake t = time since last large earthquake Mathematics JBR et al., Physical Review E, 86, 021106 (2012) JR Holliday et al, in review (2014) Conditional Weibull probability in natural time of next M event, where n = # of mevents since last M event. Δn= number of future m events ν= Time average rate of m events f= factor defined later

  20. “Elastic Rebound” in NTW • JBR et al., Physical Review E, 86, 021106 (2012) • JR Holliday et al, in review (2014) Before After Probability generally increases with time after the last large earthquake. However, finite correlation length allows large distant earthquakes to partially reduce the count n(t) thereby decreasing P(t,Δt) Probability just after the last large earthquake nearby is suddenly decreased because the count of small earthquakes n(t) = 0 at t= 0+ Thus:

  21. Probabilities are Perturbations on Time Average JBR et al., Physical Review E, 86, 021106 (2012) JR Holliday et al, in review (2014) f= factor defined so that:

  22. Filling in the Gutenberg-Richter RelationStatistics Before and After 3/11/2011Radius of 1000 km Around Tokyob=1.01 +/- 0.01 All events prior to M9.1 on 3/11/2011 (“Normal” statistics) All events after M7.7 on 3/11/2011 (Deficit of large events)