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Foundations of Statistical Seismology

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Abstract

A brief account is given of the principles of stochastic modelling in seismology, with special regard to the role and development of stochastic models for seismicity. Stochastic models are seen as arising in a hierarchy of roles in seismology, as in other scientific disciplines. At their simplest, they provide a convenient descriptive tool for summarizing data patterns; in engineering and other applications, they provide a practical way of bridging the gap between the detailed modelling of a complex system, and the need to fit models to limited data; at the most fundamental level they arise as a basic component in the modelling of earthquake phenomena, analogous to that of stochastic models in statistical mechanics or turbulence theory. As an emerging subdiscipline, statistical seismology includes elements of all of these. The scope for the development of stochastic models depends crucially on the quantity and quality of the available data. The availability of extensive, high-quality catalogues and other relevant data lies behind the recent explosion of interest in statistical seismology. At just such a stage, it seems important to review the underlying principles on which statistical modelling is based, and that is the main purpose of the present paper.

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Acknowledgements

This paper has grown out of many years of thinking about stochastic models for earthquakes, starting from the encouragement initially given to a green young statistician by Frank Evison, and sustained since then by many colleagues: Yan Kagan, Cinna Lomnitz, Yosi Ogata, David Harte, Mark Bebbington and many others.

A preliminary version of this paper was presented to the 5th Statsei Meeting in Erice, Sicily, in June 2007.

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  1. Statistical Research Associates, Victoria University, Wellington, New Zealand

    David Vere-Jones

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  1. David Vere-Jones
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Correspondence to David Vere-Jones.

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Vere-Jones, D. Foundations of Statistical Seismology. Pure Appl. Geophys. 167, 645–653 (2010). https://doi.org/10.1007/s00024-010-0079-z

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  • DOI: https://doi.org/10.1007/s00024-010-0079-z

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