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

• Altri autori: Gentili, Stefania (Redattore), Giovambattista, Rita Di (Redattore), Shcherbakov, Robert (Redattore), Vallianatos, Filippos (Redattore)
• Natura: Elettronico Capitolo di libro
• Lingua: inglese
• Pubblicazione: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Soggetti: 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
• Accesso online: 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...