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

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...

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Bibliographic Details
Other Authors:	Gentili, Stefania (Editor), Giovambattista, Rita Di (Other), Shcherbakov, Robert, Vallianatos, Filippos
Format:	eBook
Language:	Inglés
Published:	Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:	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
See on Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009741337506719