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Testing prediction methods: Earthquake clustering versus the Poisson model

January 1, 1997

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.

Publication Year 1997
Title Testing prediction methods: Earthquake clustering versus the Poisson model
DOI 10.1029/97GL01928
Authors A.J. Michael
Publication Type Article
Publication Subtype Journal Article
Series Title Geophysical Research Letters
Index ID 70019218
Record Source USGS Publications Warehouse