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

• Outros Autores: Gentili, Stefania (Editor), Giovambattista, Rita Di (Editor), Shcherbakov, Robert (Editor), Vallianatos, Filippos (Editor)
• Formato: Recurso Electrónico Capítulo de Livro
• Idioma: inglês
• Publicado em: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Assuntos: Technology: general issues; Environmental science, engineering & technology; 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; 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; numerical modeling; earthquake simulator; earthquake clustering; northern and central Apennines; n/a
• Acesso em linha: DOAB: download the publication; DOAB: description of the publication

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 spatiotempor...