Statistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity

Edited by
June 2022
180 pages
  • ISBN978-3-0365-4263-8 (Hardback)
  • ISBN978-3-0365-4264-5 (PDF)

This book is a reprint of the Special Issue Statistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Environmental & Earth Sciences
Physical Sciences

Due to the significant increase in the availability of new data in recent years, as a result of the expansion of available seismic stations, laboratory experiments, and the availability of increasingly reliable synthetic catalogs, considerable progress has been made in understanding the spatiotemporal properties of earthquakes. The study of the preparatory phase of earthquakes and the analysis of past seismicity has led to the formulation of seismicity models for the forecasting of future earthquakes or to the development of seismic hazard maps. The results are tested and validated by increasingly accurate statistical methods. A relevant part of the development of many models is the correct identification of seismicity clusters and scaling laws of background seismicity. In this collection, we present eight innovative papers that address all the above topics.

The occurrence of strong earthquakes (mainshocks) is analyzed from different perspectives in this Special Issue.

  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
system-analytical method; earthquake-prone areas; pattern recognition; clustering; machine learning; earthquake catalogs; high seismicity criteria; tidal triggering of earthquakes; seismic cycle; coulomb failure stress; preparatory phase; seismic prediction; earthquake forecasting; precursors; statistical seismology; earthquake likelihood models; seismicity patterns; New Zealand; California; smoothed seismicity methods; global seismicity; foreshocks and aftershocks; earthquake forecasting model; statistical methods; statistical seismology; magnitude-frequency distribution; corner magnitude; tapered Pareto; tapered Gutenberg-Richter; epidemic type aftershock sequence model; extreme value distribution; Bayesian predictive distribution; seismicity clustering; DBSCAN algorithm; markovian arrival processes; statistical seismology; numerical modeling; earthquake simulator; statistical methods; earthquake clustering; northern and central Apennines; n/a