Geochemical Mechanics and Deep Neural Network Modeling: Applications to Earthquake Prediction

Description

The recent understandings about global earth mechanics are widely based on huge amounts of monitoring data accumulated using global networks of precise seismic stations, satellite monitoring of gravity, very large baseline interferometry, and the Global Positioning System. New discoveries in materials sciences of rocks and minerals and of rock deformation with fluid water in the earth also provide essential information. This book presents recent work on natural geometry, spatial and temporal distribution patterns of various cracks sealed by minerals, and time scales of their crack sealing in the plate boundary. Furthermore, the book includes a challenging investigation of stochastic earthquake prediction testing by means of the updated deep machine learning of a convolutional neural network with multi-labeling of large earthquakes and of the generative autoencoder modeling of global correlated seismicity. Their manifestation in this book contributes to the development of human society resilient from natural hazards. Presented here are (1) mechanics of natural crack sealing and fluid flow in the plate boundary regions, (2) large-scale permeable convection of the plate boundary, (3) the rapid process of massive extrusion of plate boundary rocks, (4) synchronous satellite gravity and global correlated seismicity, (5) Gaussian network dynamics of global correlated seismicity, and (6) prediction testing of plate boundary earthquakes by machine learning and generative autoencoders. Preface Acknowledgements Contents 1 Introduction 1.1 Introduction to Geochemical Mechanics and Deep Neural Network Prediction of Earthquake References 2 Mechanics of Crack Sealing with Fluid Flow in the Plate Boundary 2.1 Geometry and Distribution of Naturally Sealed Cracks 2.2 Successive Plastic Deformation and Temporal Distribution of Sealed Open Cracks 2.3 Crack Sealing of the Subduction Boundary Rocks 2.4 Shear Crack Growth and Propagation with Fluid Flow 2.5 Adjoint Instability of Sealed Cracks and Velocity Change of Crack Growth in the Plate Boundary Rocks 2.6 Serpentine Sealing Open Crack in the Wedge and Slab Mantle 2.7 Waveform Outline (Bowing) of Sealed Open Crack Tip References 3 Large-Scale Permeable Convection in the Plate Boundary Zone 3.1 Damage Zone and Sealed Crack Along the Plate Boundary 3.2 Porosity and Permeability of the Plate Boundary Derived from Open Crack and Shear Crack Jog Density 3.3 2D Convection of Porous Filling Fluid of the Plate Boundary Zone 3.4 Geochemical Periodicity of Inflow and Outflow of Fluid Along the Subduction Zone 3.5 Periodicity of Fluid Composition and Mechanical Coupling of the Subduction Zone References 4 Rapid Process of Massive Extrusion of Plate Boundary Rocks 4.1 Rapid Process of the Subduction Boundary Zone 4.2 Time Scales of Spherical Shape Transformation and Waveform Grain Boundary During Growth 4.3 Rapid and Extremely Rapid Extrusion of Massive Metamorphic Rocks 4.4 Time Scale of Mineral Banding and Metasomatic Instability with Fluid Flow 4.5 Dynamics of Porosity Wave and Mineral Banding 4.6 Time Scale of Konpeito-Like Flower Grain Growth and Fluid Flow References 5 Mechanics by Synchronous GRACE Gravity, Earth Rotation, Plate Velocity, and Global Correlated Seismicity 5.1 Global Monitoring Data and Seismicity 5.2 Gaussian Regression of the GRACE Gravity Data 5.3 Temporal Variation of the Global Correlated Seismicity 5.4 Synchronous Change of the Global Satellite Gravity, Earth Rotation, and Correlated Seismicity 5.5 Synchronous Change of the VLBI Geodesical Data and Correlated Seismicity 5.6 Periodic Variation of Correlated Seismicity and Global C20 of the Japanese Islands Region 5.7 Nonlinear Dynamics of Earth Rotation and Global Correlated Seismicity References 6 Gaussian Network Model of Global Seismicity 6.1 Phase Transition and Fluctuation in Gaussian Network Dynamics 6.2 Geometrical Transformation of Gaussian Network of Global Seismicity 6.3 Gaussian Network Dynamics of the Japanese Region References 7 Prediction Testing of Plate Boundary Earthquake by Global DCNN and VAE-CNN Modeling 7.1 Possibility of Probabilistic Prediction of the Large Earthquake 7.2 Preprocessing of Seismic Source Data 7.3 DCNN with Multilabel Modeling Analysis of Large Earthquake Event 7.4 Deep Convolution Neural Network and Recurrent Neural Network with Labeling Model 7.5 Prediction Testing of the Large Earthquakes in the Global Subduction Zones 7.6 DCNN with Variational Timestep and Time-Shift Method (VTTM) Modeling 7.7 Prediction Testing of M5 Over Earthquakes in the Japanese Region by Correlated Seismicity 7.8 Feature Mapping of DCNN Intermediate Layer Output 7.9 Feature Mapping in Latent Space of Generative Variational Autoencoder (GVAE-DNN, VAE-CNN) 7.10 Future Strategy of Earthquake Prediction Testing References 8 Conclusive Remarks 8.1 Conclusive Remarks References Appendix