4.6 Article

Comparison of characteristic and Gutenberg-Richter models for time-dependent M=7.9 earthquake probability in the Nankai-Tokai subduction zone, Japan

期刊

GEOPHYSICAL JOURNAL INTERNATIONAL
卷 190, 期 3, 页码 1673-1688

出版社

OXFORD UNIV PRESS
DOI: 10.1111/j.1365-246X.2012.05595.x

关键词

Time series analysis; Spatial analysis; Probability distributions; Seismic cycle; Earthquake interaction; forecasting; and prediction; Statistical seismology

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Earthquake forecasts are usually underinformed, and can be plagued by uncertainty in terms of the most appropriate model, and parameter values used in that model. In this paper, we explore the application of two different models to the same seismogenic area. The first is a renewal model based on the characteristic earthquake hypothesis that uses historical/palaeoseismic recurrence times, and fixed rupture geometries. The hazard rate is modified by the Coulomb static stress change caused by nearby earthquakes that occurred since the latest characteristic earthquake. The second model is a very simple earthquake simulator based on plate-motion, or fault-slip rates and adoption of a GutenbergRichter magnitudefrequency distribution. This information is commonly available even if historical and palaeoseismic recurrence data are lacking. The intention is to develop and assess a simulator that has a very limited parameter set that could be used to calculate earthquake rates in settings that are not as rich with observations of large-earthquake recurrence behaviour as the Nankai trough. We find that the use of convergence rate as a primary constraint allows the simulator to replicate much of the spatial distribution of observed segmented rupture rates along the Nankai, Tonankai and Tokai subduction zones. Although we note rate differences between the two forecast methods in the Tokai zone, we also see enough similarities between simulations and observations to suggest that very simple earthquake rupture simulations based on empirical data and fundamental earthquake laws could be useful forecast tools in information-poor settings.

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