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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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Guardat en:
Format: Online
Idioma:anglès
Publicat: MDPI - Multidisciplinary Digital Publishing Institute 2022
Matèries:
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
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology
Accés en línia:ONIX_20220706_9783036542638_75
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