Problems for Probabilistic Seismic Hazard Analysis

Speidel, D. H.; Mattson, Peter H.

doi:10.1007/978-94-011-5034-7_5

Cited by 2 publications

(2 citation statements)

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“…Some authors have noted that paleoseismological data record larger magnitude events than would be predicted by extrapolation of the GR model [e.g., Speidel and Mattson , 1997]. These outliers can be explained by a combination of the apparent positive bias for small samples at high magnitudes (even the best palaeoseismic records rarely contain more than 10 events), and the preferential preservation of such events in the geological record.…”

Section: Discussionmentioning

confidence: 99%

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Statistical evaluation of characteristic earthquakes in the frequency‐magnitude distributions of Sumatra and other subduction zone regions

Naylor¹,

Greenhough²,

McCloskey³

et al. 2009

Geophysical Research Letters

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If subduction zone earthquakes conform to a characteristic model, in which persistent segments fail at predictable stress levels due to the steady accumulation of tectonic loading, historical seismicity may constrain the occurrence of future events. We test this model for earthquakes on the Sumatra‐Andaman megathrust and other subduction zones using frequency‐magnitude distributions. Using simulations, we show that Poisson confidence intervals correctly account for the counting errors of histogram data. These confidence intervals demonstrate that we cannot reject the Gutenberg‐Richter distribution in favor of a characteristic model in any of the real catalogues tested. A visual bias in power‐law count data at high magnitudes, combined with a sample bias for large earthquakes, is sufficient to explain candidate characteristic events. This result implies that historical earthquakes are likely poor models for future events and that Monte Carlo simulations will provide a better assessment of earthquake and associated hazards.

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Section: Discussionmentioning

confidence: 99%

“…However, in many cases the evidence remains qualitative [e.g., Schwartz and Coppersmith , 1984]. Where formal statistical tests are done, it is common to neglect potential biases from the effects of finite sample size and to assume that residuals are Gaussian distributed [e.g., Speidel and Mattson , 1997].…”

Section: Introductionmentioning

confidence: 99%