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Geochemical Mechanics And Deep Neural Network Modeling Applications To Earthquake Prediction Mitsuhiro Toriumi

  • SKU: BELL-44831814
Geochemical Mechanics And Deep Neural Network Modeling Applications To Earthquake Prediction Mitsuhiro Toriumi
$ 31.00 $ 45.00 (-31%)

4.7

16 reviews

Geochemical Mechanics And Deep Neural Network Modeling Applications To Earthquake Prediction Mitsuhiro Toriumi instant download after payment.

Publisher: Springer
File Extension: PDF
File size: 12.97 MB
Pages: 282
Author: Mitsuhiro Toriumi
ISBN: 9789811936586, 9811936587
Language: English
Year: 2022

Product desciption

Geochemical Mechanics And Deep Neural Network Modeling Applications To Earthquake Prediction Mitsuhiro Toriumi by Mitsuhiro Toriumi 9789811936586, 9811936587 instant download after payment.

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.

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