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Global seismicity dynamics and data-driven scienceseismicity modelling by big data analytics /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Global seismicity dynamics and data-driven scienceby Mitsuhiro Toriumi.
Reminder of title:	seismicity modelling by big data analytics /
Author:	Toriumi, Mitsuhiro.
Published:	Singapore :Springer Singapore :2021.
Description:	ix, 231 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	SeismologyData processing.
Online resource:	https://doi.org/10.1007/978-981-15-5109-3
ISBN:	9789811551093$q(electronic bk.)

Global seismicity dynamics and data-driven scienceseismicity modelling by big data analytics /
Toriumi, Mitsuhiro.

Global seismicity dynamics and data-driven scienceseismicity modelling by big data analytics /[electronic resource] :by Mitsuhiro Toriumi. - Singapore :Springer Singapore :2021. - ix, 231 p. :ill., digital ;24 cm. - Advances in geological science,2524-3829. - Advances in geological science..

- 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.

The 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.

ISBN: 9789811551093$q(electronic bk.)

Standard No.: 10.1007/978-981-15-5109-3doiSubjects--Topical Terms:

485145
Seismology
--Data processing.

LC Class. No.: QE539 / .T67 2021

Dewey Class. No.: 551.22

Global seismicity dynamics and data-driven scienceseismicity modelling by big data analytics /
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505 0 $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.
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