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Exploratory analysis of earthquake clusters by likelihood-based trigger models

Part of: Inference from stochastic processes Geophysics Geophysics (general) Stochastic processes

Published online by Cambridge University Press:  14 July 2016

Yosihiko Ogata
Yosihiko Ogata*
Affiliation:
Institute of Statistical Mathematics, Tokyo
*
1 Postal address: Institute of Statistical Mathematics, 4–6–7 Minami-azabu, Minatoku, Tokyo 106, Japan. Email: ogata@ism.ac.jp
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Abstract

The paper considers the superposition of modified Omori functions as a conditional intensity function for a point process model used in the exploratory analysis of earthquake clusters. For the examples discussed, the maximum likelihood estimates converge well starting from appropriate initial values even though the number of parameters estimated can be large (though never larger than the number of observations). Three datasets are subjected to different analyses, showing the use of the model to discover and study individual clustering features.

Keywords

AftershocksclustersETAS modelmaximum likelihood estimatesmodified Omori lawtrigger model

MSC classification

Primary: 86A15: Seismology
Secondary: 60G55: Point processes 62M09: Non-Markovian processes

Information

Type
Models and statistics in seismology
Information
Journal of Applied Probability , Volume 38 , Issue A: Probability, Statistics and Seismology , 2001 , pp. 202 - 212
Copyright
Copyright © Applied Probability Trust 2001 

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