Model of Seismicity Dynamics from Data-Driven Science
The multivariate time series of the correlated seismicity rates newly obtained by sparse modeling in the global and regional systems are potential indicators of seismicity dynamics. The sets of these parameters with successive time are mapped and imaged a
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Mitsuhiro Toriumi
Global Seismicity Dynamics and Data-Driven Science Seismicity Modelling by Big Data Analytics
Advances in Geological Science Series Editors Junzo Kasahara, Tokyo University of Marine Science and Technology, Tokyo, Japan; Shizuoka University, Shizuoka, Japan Michael Zhdanov, University of Utah, Utah, USA Tuncay Taymaz, Istanbul Technical University, Istanbul, Turkey
Studies in the twentieth century uncovered groundbreaking facts in geophysics and produced a radically new picture of the Earth’s history. However, in some respects it also created more puzzles for the research community of the twenty-first century to tackle. This book series aims to present the state of the art of contemporary geological studies and offers the opportunity to discuss major open problems in geosciences and their phenomena. The main focus is on physical geological features such as geomorphology, petrology, sedimentology, geotectonics, volcanology, seismology, glaciology, and their environmental impacts. The monographs in the series, including multi-authored volumes, will examine prominent features of past events up to their current status, and possibly forecast some aspects of the foreseeable future. The guiding principle is that understanding the fundamentals and applied methodology of overlapping fields will be key to paving the way for the next generation.
More information about this series at http://www.springer.com/series/11723
Mitsuhiro Toriumi
Global Seismicity Dynamics and Data-Driven Science Seismicity Modelling by Big Data Analytics
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Mitsuhiro Toriumi Japan Agency of Marine-Earth Science and Technology Yokohama, Kanagawa, Japan
ISSN 2524-3829 ISSN 2524-3837 (electronic) Advances in Geological Science ISBN 978-981-15-5108-6 ISBN 978-981-15-5109-3 (eBook) https://doi.org/10.1007/978-981-15-5109-3 © Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in publish
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