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Physics of Geochemical Mechanics and Deep Neural Network Modeling with Diffusion Augmentation

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Release : 2024-12-07
Genre : Computers
Kind : eBook
Book Rating : 754/5 ( reviews)

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Book Synopsis Physics of Geochemical Mechanics and Deep Neural Network Modeling with Diffusion Augmentation by : Mitsuhiro Toriumi

Download or read book Physics of Geochemical Mechanics and Deep Neural Network Modeling with Diffusion Augmentation written by Mitsuhiro Toriumi. This book was released on 2024-12-07. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Geochemical Mechanics and Deep Neural Network Modeling

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Release : 2023-08-21
Genre : Science
Kind : eBook
Book Rating : 616/5 ( reviews)

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Book Synopsis Geochemical Mechanics and Deep Neural Network Modeling by : Mitsuhiro Toriumi

Download or read book Geochemical Mechanics and Deep Neural Network Modeling written by Mitsuhiro Toriumi. This book was released on 2023-08-21. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Dissertation Abstracts International

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Author :
Release : 1989
Genre : Dissertations, Academic
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Dissertation Abstracts International by :

Download or read book Dissertation Abstracts International written by . This book was released on 1989. Available in PDF, EPUB and Kindle. Book excerpt:

Gaussian Processes for Machine Learning

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Release : 2005-11-23
Genre : Computers
Kind : eBook
Book Rating : 53X/5 ( reviews)

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Book Synopsis Gaussian Processes for Machine Learning by : Carl Edward Rasmussen

Download or read book Gaussian Processes for Machine Learning written by Carl Edward Rasmussen. This book was released on 2005-11-23. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

Global Seismicity Dynamics and Data-Driven Science

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Release : 2020-10-07
Genre : Science
Kind : eBook
Book Rating : 09X/5 ( reviews)

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Book Synopsis Global Seismicity Dynamics and Data-Driven Science by : Mitsuhiro Toriumi

Download or read book Global Seismicity Dynamics and Data-Driven Science written by Mitsuhiro Toriumi. This book was released on 2020-10-07. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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