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.
Author(s): Mitsuhiro Toriumi
Series: Advances in Geological Science
Publisher: Springer Singapore
Year: 2020
Language: English
Pages: 278
City: Singapore
Preface
Contents
1 Introduction
1.1 Introduction
References
2 Nature of Earthquake in the Solid Earth
2.1 Global Earthquake Distribution and Plate Tectonics
2.2 Earthquake Propagation and Shear Instability
2.3 Earthquakes and Global Network of Seismic Stations
References
3 Global Seismicity of the Solid Earth
3.1 Stochastic Natures of Seismicity
3.2 Two Types of Earthquakes and Their Occurrences
3.3 The Global Seismicity of Subduction Zones
3.4 The Global Seismicity of Mid-Oceanic Ridges
3.5 Global Moment Release Rates by Large Earthquakes
3.6 Stress Orientation and Seismic Anisotropy of the Plate Boundary
References
4 Data-Driven Science for Geosciences
4.1 Matrix Decomposition Method and Sparse Modeling
4.2 Deep Neural Network Approximation
4.3 State-Space Modeling of Time Series
4.4 Frobenius Norm Minimum Method for Dynamics
References
5 Data-Driven Science of Global Seismicity
5.1 Data Cloud of the Global and Japanese Seismicity
5.2 Data-Driven Science of Global Seismicity Dynamics
5.3 The Characteristic Features of Strongly Correlated Global Seismicity
5.4 Global Seismic Moment Release Rate and Correlated Seismicity Rates
5.5 Correlated Seismicity Rate Variations of Global Ocean Ridges
5.6 Global Seismic Activity of Subduction Zones and Oceanic Ridges
References
6 Correlated Seismicity of Japanese Regions
6.1 Outline of Tectonics of the Japanese Islands
6.2 Seismicity of Japanese Islands Region
6.3 Seismicity Cloud of Japanese Islands Crust and Mantle
6.4 Characteristic Features of the Correlated Seismicity Rates
6.5 Characteristic Features of Correlated Seismicity Rate Time Series
6.6 Correlated Seismicity Rates on the z1–z2–z3 Diagram
6.7 Coherency of Correlated Seismicity Rates Between Mantle and Crust
6.8 Annual Variation of the Correlated Seismicity Rate
6.9 Annual Variation of the Partial b-Value Time Series
6.10 Correlated Seismicity of Non-snowy and Snowy Regions of Japanese Islands
6.11 Partial b123 and b234 Value and Correlated Seismicity Rates
6.12 Correlated Seismicity Between the Global and Japanese Islands Region
References
7 Correlated Seismicity of the Northern California Region
7.1 Introduction
7.2 Seismicity Cloud of the Northern California Region
7.3 Correlated Seismicity Rates in Northern California
7.4 Partial b-Value Variations of the Northern California Region
7.5 Comparison Between Global Subduction Zones and Northern California Region
References
8 Model of Seismicity Dynamics from Data-Driven Science
8.1 Minimal Model of Global Seismicity Dynamics
8.2 Synthetic Coherency of Seismicity Dynamics by Slider Block Model
References
9 Seismicity Dynamics Model of Global Earth and Japanese Islands Region
9.1 Minimal Model of the Global and Japanese Seismicity Dynamics
9.2 Minimal Dynamic Model of Japanese Correlated Seismicity
9.3 Partial b-Value Change and Its Annual Variation
References
10 Prediction Modeling of Global and Regional Seismicity Rates
10.1 State-Space Modeling of the Global and Japanese Seismicity Dynamics
10.2 Inversion of the Global Seismicity Rates from Correlated Seismicity
10.3 Data-Driven Science and Machine Learning of Global Seismicity
10.4 The Main Sequence of Relations Between Global Correlated Seismicity Rates and Local Seismicity Rates: The Cases of Japanese Islands, Sumatra and Chile
10.5 Global Seismicity Dynamics and Plate Tectonics
10.6 Possibility of Deep Learning Recurrent Neural Network for Prediction of the Seismic Activity
10.7 System Model of the Correlated Seismicity, Plate Boundary Slip, and Fluid Flux in the Subduction Zone
References
11 Problems of Prediction for Giant Plate Boundary Earthquakes
References
Appendix A Application of Recurrent Neural Network (RNN) Modeling for Global Seismicity Dynamics
Appendix B Comments on Databases and Software Used in This Book
