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Uncertainty in Seismic Hazard Assessments (Truth in Advertising/Full Disclosure)

Figure A3–1. Uncertainty in Seismic Hazard Assessments (Truth in Advertising/Full Disclosure). M MAX Workshop USGS-Golden September 8/9, 2008 Jon Ake, U.S. NRC. Figure A3–2. Uncertainty in Seismic Hazard Assessments.

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Uncertainty in Seismic Hazard Assessments (Truth in Advertising/Full Disclosure)

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  1. Figure A3–1 Uncertainty in Seismic Hazard Assessments(Truth in Advertising/Full Disclosure) MMAX Workshop USGS-Golden September 8/9, 2008 Jon Ake, U.S. NRC

  2. Figure A3–2 Uncertainty in Seismic Hazard Assessments • Regulatory Requirements/Guidance: 10 CFR 100.23 recognizes the nature of uncertainty in seismological and geological evaluations and the need to account for these uncertainties. Further developed in Regulatory Guides 1.165 and 1.208. • Similar considerations are imbedded in DOE Standards, recent ANS/ANSI Standards, ICODS Guidelines • Good Practice

  3. Figure A3–3 Uncertainty and Variability 1. Aleatory Variability -The natural randomness in a process -Not predictable -Can’t be reduced (theoretically) 2. Epistemic Uncertainty -Knowledge-based -Unknown models/parameters -Competing models -Potentially reducible -Generally incorporated via logic trees

  4. Figure A3–4 Examples Epistemic: Alternative models to predict magnitude based on Intensity -Maximum Intensity (MMIMAX) vs Area within a MMI contour (AI-VI) -Each of these models has an uncertainty associated with it. Epistemic: Alternative models to predict magnitude based on fault characteristics -Fault length vs displacement -Each of these models has an estimate of variability associated with it

  5. Figure A3–5 Uncertainty in Seismic Hazard Assessments • Need to take a pragmatic approach • A snapshot in time • Problems especially acute in SCR • Space for time substitution is a given (both for development of models and estimates of variability)

  6. Figure A3–6 Summary • Necessary to formally include uncertainty/variability in our assessments • We need to develop a well-defined process that will include and track the aleatory and epistemic uncertainties, but avoid “double-counting” of uncertainty • Need to define credible alternative models

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