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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Other Authors: Gentili, Stefania (Editor), Giovambattista, Rita Di (Editor), Shcherbakov, Robert (Editor), Vallianatos, Filippos (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
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DOAB: description of the publication
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700 1 |a Vallianatos, Filippos  |4 oth 
245 1 0 |a Statistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity 
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520 |a 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. 
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546 |a English 
650 7 |a Technology: general issues  |2 bicssc 
650 7 |a Environmental science, engineering & technology  |2 bicssc 
653 |a system-analytical method 
653 |a earthquake-prone areas 
653 |a pattern recognition 
653 |a clustering 
653 |a machine learning 
653 |a earthquake catalogs 
653 |a high seismicity criteria 
653 |a tidal triggering of earthquakes 
653 |a seismic cycle 
653 |a coulomb failure stress 
653 |a preparatory phase 
653 |a seismic prediction 
653 |a earthquake forecasting 
653 |a precursors 
653 |a statistical seismology 
653 |a earthquake likelihood models 
653 |a seismicity patterns 
653 |a New Zealand 
653 |a California 
653 |a smoothed seismicity methods 
653 |a global seismicity 
653 |a foreshocks and aftershocks 
653 |a earthquake forecasting model 
653 |a statistical methods 
653 |a magnitude-frequency distribution 
653 |a corner magnitude 
653 |a tapered Pareto 
653 |a tapered Gutenberg-Richter 
653 |a epidemic type aftershock sequence model 
653 |a extreme value distribution 
653 |a Bayesian predictive distribution 
653 |a seismicity clustering 
653 |a DBSCAN algorithm 
653 |a markovian arrival processes 
653 |a numerical modeling 
653 |a earthquake simulator 
653 |a earthquake clustering 
653 |a northern and central Apennines 
653 |a n/a 
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856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/87480  |7 0  |z DOAB: description of the publication