LEADER 03968nam 2200877z- 450 001 9910674369603321 005 20231214133221.0 035 $a(CKB)5690000000012032 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/87480 035 $a(EXLCZ)995690000000012032 100 $a20202207d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (180 p.) 311 $a3-0365-4263-9 311 $a3-0365-4264-7 330 $aDue 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. 606 $aTechnology: general issues$2bicssc 606 $aEnvironmental science, engineering & technology$2bicssc 610 $asystem-analytical method 610 $aearthquake-prone areas 610 $apattern recognition 610 $aclustering 610 $amachine learning 610 $aearthquake catalogs 610 $ahigh seismicity criteria 610 $atidal triggering of earthquakes 610 $aseismic cycle 610 $acoulomb failure stress 610 $apreparatory phase 610 $aseismic prediction 610 $aearthquake forecasting 610 $aprecursors 610 $astatistical seismology 610 $aearthquake likelihood models 610 $aseismicity patterns 610 $aNew Zealand 610 $aCalifornia 610 $asmoothed seismicity methods 610 $aglobal seismicity 610 $aforeshocks and aftershocks 610 $aearthquake forecasting model 610 $astatistical methods 610 $amagnitude-frequency distribution 610 $acorner magnitude 610 $atapered Pareto 610 $atapered Gutenberg-Richter 610 $aepidemic type aftershock sequence model 610 $aextreme value distribution 610 $aBayesian predictive distribution 610 $aseismicity clustering 610 $aDBSCAN algorithm 610 $amarkovian arrival processes 610 $anumerical modeling 610 $aearthquake simulator 610 $aearthquake clustering 610 $anorthern and central Apennines 615 7$aTechnology: general issues 615 7$aEnvironmental science, engineering & technology 700 $aGentili$b Stefania$4edt$01338656 702 $aGiovambattista$b Rita Di$4edt 702 $aShcherbakov$b Robert$4edt 702 $aVallianatos$b Filippos$4edt 702 $aGentili$b Stefania$4oth 702 $aGiovambattista$b Rita Di$4oth 702 $aShcherbakov$b Robert$4oth 702 $aVallianatos$b Filippos$4oth 906 $aBOOK 912 $a9910674369603321 996 $aStatistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity$93058893 997 $aUNINA