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

Mark Naylor, John Greenhough, John McCloskey, Andrew A.F. Bell, Ian Main

    Research output: Contribution to journalArticle

    29 Citations (Scopus)

    Abstract

    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.
    LanguageEnglish
    JournalGeophysical Research Letters
    Volume36
    Issue numberL20303
    DOIs
    Publication statusPublished - 17 Oct 2009

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    frequency-magnitude distribution
    subduction zone
    earthquake
    confidence interval
    histogram
    simulation
    seismicity
    power law
    evaluation
    hazard
    tectonics

    Cite this

    Naylor, Mark ; Greenhough, John ; McCloskey, John ; A.F. Bell, Andrew ; Main, Ian. / Statistical evaluation of characteristic earthquakes in the frequency-magnitude distributions of Sumatra and other subduction zone regions. In: Geophysical Research Letters. 2009 ; Vol. 36, No. L20303.
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    Statistical evaluation of characteristic earthquakes in the frequency-magnitude distributions of Sumatra and other subduction zone regions. / Naylor, Mark; Greenhough, John; McCloskey, John; A.F. Bell, Andrew; Main, Ian.

    In: Geophysical Research Letters, Vol. 36, No. L20303, 17.10.2009.

    Research output: Contribution to journalArticle

    TY - JOUR

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    AU - Naylor, Mark

    AU - Greenhough, John

    AU - McCloskey, John

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    AU - Main, Ian

    PY - 2009/10/17

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    N2 - 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.

    AB - 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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