LEADER 03737nam 2200457 450 001 9910484884103321 005 20210305132743.0 010 $a981-15-5109-X 024 7 $a10.1007/978-981-15-5109-3 035 $a(CKB)4100000011493418 035 $a(DE-He213)978-981-15-5109-3 035 $a(MiAaPQ)EBC6369627 035 $a(PPN)260305898 035 $a(EXLCZ)994100000011493418 100 $a20210305d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGlobal seismicity dynamics and data-driven science $eseismicity modelling by big data analytics /$fMitsuhiro Toriumi 205 $a1st ed. 2021. 210 1$aGateway East, Singapore :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (IX, 231 p. 230 illus., 184 illus. in color.) 225 1 $aAdvances in Geological Science,$x2524-3829 311 $a981-15-5108-1 327 $a- Introduction -- Nature of Earthquakes in the Solid Earth -- Global Seismicity of the Solid Earth -- Data ? Driven Sciences for Geosciences -- Data-Driven Science of Seismicity -- Down Scaling Seismicity of Japanese Regions -- Correlated Seismicity of the Northern California Region -- Model of Seismicity Dynamics from Data-Driven Science -- Seismicity Dynamics Model of Global Earth and Japanese Island Region -- Predictive Modeling of Global and Regional Seismicity Rates -- Future Problems of Prediction of Giant Plate Boundary Earthquakes -- Application of Recurrent Neural Network (RNN) Modeling for Global Seismicity Dynamics -- Comments on Databases and Software Used in This Book. 330 $aThe recent explosion of global and regional seismicity data in the world requires new methods of investigation of microseismicity and development of their modelling to understand the nature of whole earth mechanics. In this book, the author proposes a powerful tool to reveal the characteristic features of global and regional microseismicity big data accumulated in the databases of the world. The method proposed in this monograph is based on (1) transformation of stored big data to seismicity density data archives, (2) linear transformation of microseismicity density data matrixes to correlated seismicity matrixes by means of the singular value decomposition method, (3) time series analyses of globally and regionally correlated seismicity rates, and (4) the minimal non-linear equations approximation of their correlated seismicity rate dynamics. Minimal non-linear modelling is the manifestation for strongly correlated seismicity time series controlled by Langevin-type stochastic dynamic equations involving deterministic terms and random Gaussian noises. A deterministic term is composed minimally with correlated seismicity rate vectors of a linear term and of a term with a third exponent. Thus, the dynamics of correlated seismicity in the world contains linearly changing stable nodes and rapid transitions between them with transient states. This book contains discussions of future possibilities of stochastic extrapolations of global and regional seismicity in order to reduce earthquake disasters worldwide. The dataset files are available online and can be downloaded at springer.com. 410 0$aAdvances in Geological Science,$x2524-3829 606 $aSeismology$xData processing 615 0$aSeismology$xData processing. 676 $a551.220285 700 $aToriumi$b Mitsuhiro$f1946-$01076514 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484884103321 996 $aGlobal Seismicity Dynamics and Data-Driven Science$92587188 997 $aUNINA