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Testing prediction methods: Earthquake clustering versus the Poisson model

Geophysical Research Letters

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Abstract

Testing earthquake prediction methods requires statistical techniques that compare observed success to random chance. One technique is to produce simulated earthquake catalogs and measure the relative success of predicting real and simulated earthquakes. The accuracy of these tests depends on the validity of the statistical model used to simulate the earthquakes. This study tests the effect of clustering in the statistical earthquake model on the results. Three simulation models were used to produce significance levels for a VLF earthquake prediction method. As the degree of simulated clustering increases, the statistical significance drops. Hence, the use of a seismicity model with insufficient clustering can lead to overly optimistic results. A successful method must pass the statistical tests with a model that fully replicates the observed clustering. However, a method can be rejected based on tests with a model that contains insufficient clustering. U.S. copyright. Published in 1997 by the American Geophysical Union.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Testing prediction methods: Earthquake clustering versus the Poisson model
Series title:
Geophysical Research Letters
Volume
24
Issue:
15
Year Published:
1997
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Geophysical Research Letters
First page:
1891
Last page:
1894
Number of Pages:
4