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Physics of geochemical mechanics and...

Toriumi, Mitsuhiro.

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Physics of geochemical mechanics and deep neural network modeling with diffusion augmentation = applications to earthquake prediction /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Physics of geochemical mechanics and deep neural network modeling with diffusion augmentation/ by Mitsuhiro Toriumi.
Reminder of title:	applications to earthquake prediction /
Author:	Toriumi, Mitsuhiro.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xiv, 288 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Physics of Geochemical Mechanics -- Characteristic Microstructures Reated to Multiphase Shear Flow -- Recent Variations of Global and Regional Correlated Seismicity -- Neural Network Modeling of Regression in Nonlinear Dynamics Timeseries -- Augmentation of Timeseries and DNN Modeling of Seismic Activity -- Concluding Remarks.
Contained By:	Springer Nature eBook
Subject:	Geophysics. -
Online resource:	https://doi.org/10.1007/978-981-97-9376-1
ISBN:	9789819793761

Physics of geochemical mechanics and deep neural network modeling with diffusion augmentation = applications to earthquake prediction /
Toriumi, Mitsuhiro.

Physics of geochemical mechanics and deep neural network modeling with diffusion augmentationapplications to earthquake prediction /[electronic resource] :by Mitsuhiro Toriumi. - Singapore :Springer Nature Singapore :2025. - xiv, 288 p. :ill., digital ;24 cm. - Advances in geological science,2524-3837. - Advances in geological science..

Introduction -- Physics of Geochemical Mechanics -- Characteristic Microstructures Reated to Multiphase Shear Flow -- Recent Variations of Global and Regional Correlated Seismicity -- Neural Network Modeling of Regression in Nonlinear Dynamics Timeseries -- Augmentation of Timeseries and DNN Modeling of Seismic Activity -- Concluding Remarks.

This book provides a new data augmentation method based on the local stochastic distribution patterns in natural time series data of global and regional seismicity rates and their correlated seismicity rates. The augmentation procedure is called the diffusion - denoising augmentation method from the local Gaussian distribution of segmented data of long time series. This method makes it possible to apply the deep machine learning necessary to neural network prediction of rare large earthquakes in the global and regional earth system. The book presents the physical background of the processes showing the development of characteristic features in the global and regional correlated seismicity dynamics, which are manifested by the successive time series of 1990-2023. Physical processes of the correlated global seismicity change and the earth's rotation, fluctuation of plate motion, and the earth's ellipsoid ratio (C20 of satellite gravity change) are proposed in this book. The equivalency between Gaussian seismicity network dynamics and the minimal nonlinear dynamics model of correlated seismicity rates is also provided. In addition, the book contains simulated models of the shear crack jog wave, precipitation of minerals in the jog, and jog accumulation inducing shear crack propagation which leads to earthquakes in the plate boundary rocks under permeable fluid flow.

ISBN: 9789819793761

Standard No.: 10.1007/978-981-97-9376-1doiSubjects--Topical Terms:

535228
Geophysics.

LC Class. No.: QC806

Dewey Class. No.: 550

Physics of geochemical mechanics and deep neural network modeling with diffusion augmentation = applications to earthquake prediction /
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505 0 $a Introduction -- Physics of Geochemical Mechanics -- Characteristic Microstructures Reated to Multiphase Shear Flow -- Recent Variations of Global and Regional Correlated Seismicity -- Neural Network Modeling of Regression in Nonlinear Dynamics Timeseries -- Augmentation of Timeseries and DNN Modeling of Seismic Activity -- Concluding Remarks.
520 $a This book provides a new data augmentation method based on the local stochastic distribution patterns in natural time series data of global and regional seismicity rates and their correlated seismicity rates. The augmentation procedure is called the diffusion - denoising augmentation method from the local Gaussian distribution of segmented data of long time series. This method makes it possible to apply the deep machine learning necessary to neural network prediction of rare large earthquakes in the global and regional earth system. The book presents the physical background of the processes showing the development of characteristic features in the global and regional correlated seismicity dynamics, which are manifested by the successive time series of 1990-2023. Physical processes of the correlated global seismicity change and the earth's rotation, fluctuation of plate motion, and the earth's ellipsoid ratio (C20 of satellite gravity change) are proposed in this book. The equivalency between Gaussian seismicity network dynamics and the minimal nonlinear dynamics model of correlated seismicity rates is also provided. In addition, the book contains simulated models of the shear crack jog wave, precipitation of minerals in the jog, and jog accumulation inducing shear crack propagation which leads to earthquakes in the plate boundary rocks under permeable fluid flow.
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