Geochemical Mechanics and Deep Neural Network Modeling

Chapter 1 Introduction

1.1 Connection between Materials Science Involving Water and Deep Neural Network Modeling in Earthquake Prediction Testing

References

Chapter 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

References

Chapter 3 Large Scale Permeable Convection of the Plate Boundary Zone

3.1 Damage Zone and Sealed Crack along Plate Boundary

3.2 Porosity and Permeability of the Plate Boundary Derived from Open Crack and Shear Crack Jog Density

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

Chapter 4 Rapid Process of Massive Extrusion of Plate Boundary Rocks

4.1 Rapid Process in 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

Chapter 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 Non-Linear Dynamics of Earth Rotation and Global Correlated Seismicity

References

Chapter 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

Chapter 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 Pre-Processing 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

Chapter 8 Concluding Remarks

8.1 Concluding Remarks

References

Acknowledgements

Appendix --- Codes of the Deep Neural Network Modeling by Keras-Tensorflow and Gaussian Regression Modeling in Python 3 Environment