书目名称 | Geochemical Mechanics and Deep Neural Network Modeling |
副标题 | Applications to Eart |
编辑 | Mitsuhiro Toriumi |
视频video | |
概述 | Combines materials sciences with deep machine learning to develop new methods for earthquake prediction testing.Introduces recent AI modeling using a Keras–TensorFlow environment.Allows readers to lea |
丛书名称 | Advances in Geological Science |
图书封面 |  |
描述 | 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 societyresilient 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) t |
出版日期 | Book 2022 |
关键词 | Earthquake prediction testing; Convolutional neural network (CNN); Multi-labeling and variational time |
版次 | 1 |
doi | https://doi.org/10.1007/978-981-19-3659-3 |
isbn_softcover | 978-981-19-3661-6 |
isbn_ebook | 978-981-19-3659-3Series ISSN 2524-3829 Series E-ISSN 2524-3837 |
issn_series | 2524-3829 |
copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor |