Current Challenges in Statistical Seismology

This special issue emerged following the 2013 8th International Statistical Seismology (StatSei8) workshop in Beijing. The articles within have been collected to report on exciting new research in statistical seismology methods and applications; it contains a collection of the newest methods, techniques and results related to statistical analysis of earthquake occurrence and earthquake probability forecasting. The articles within ultimately help to define future research directions in the field. Especially, the rapid development of observation technologies has brought geophysical research into the big-data era. This includes not only non-seismicity geophysical data, such as GPS observation on surface displacement, InSAR observation of the co-seismic deformation, ionospheric observations, etc., but also extended seismological data including slow earthquakes, tremor, and VLF earthquakes.

The subject of statistical seismology bridges the gap between physical and statistical models. Many significant achievements have been accomplished during the last several decades, including formulation of conditional intensity models for quantifying seismicity rates, earthquake probability forecasts, and theories related to rigorous testing of forecast models.

• Earthquake probability forecast
• point process model
• information gain
• seismicity
• tectonics
• statistical seismology

• Department of Geophysics, Peking University Department of Geophysics, Beijing, China

  Qinghua Huang

• GNS Science, Avalon, New Zealand

  Matthew Gerstenberger

• Institute of Statistical Mathematics, Tokyo, Japan

  Jiancang Zhuang