Statistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity
Gentili, Stefania
Statistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity - Basel MDPI - Multidisciplinary Digital Publishing Institute 2022 - 1 electronic resource (180 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-4264-5 9783036542638 9783036542645
10.3390/books978-3-0365-4264-5 doi
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
Statistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity - Basel MDPI - Multidisciplinary Digital Publishing Institute 2022 - 1 electronic resource (180 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-4264-5 9783036542638 9783036542645
10.3390/books978-3-0365-4264-5 doi
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