國立高雄大學圖資館 |

Geochemical mechanics and deep neural network modelingapplications to earthquake prediction /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Geochemical mechanics and deep neural network modelingby Mitsuhiro Toriumi.
其他題名:	applications to earthquake prediction /
作者:	Toriumi, Mitsuhiro.
出版者:	Singapore :Springer Nature Singapore :2022.
面頁冊數:	xiii, 274 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Earthquake prediction.
電子資源:	https://doi.org/10.1007/978-981-19-3659-3
ISBN:	9789811936593$q(electronic bk.)

Geochemical mechanics and deep neural network modelingapplications to earthquake prediction /
Toriumi, Mitsuhiro.

Geochemical mechanics and deep neural network modelingapplications to earthquake prediction /[electronic resource] :by Mitsuhiro Toriumi. - Singapore :Springer Nature Singapore :2022. - xiii, 274 p. :ill., digital ;24 cm. - Advances in geological science,2524-3837. - Advances in geological science..

Introduction -- Mechanics of Crack Sealing with Fluid Flow in the Plate Boundary -- Large Scale Permeable Convection of the Plate Boundary Zone -- Rapid Process of Massive Extrusion of Plate Boundary Rocks -- Mechanics by Synchronous GRACE Gravity, Earth Rotation, Plate Velocity and Global Correlated Seismicity -- Gaussian Network Model of Global Seismicity -- Prediction Testing of Plate Boundary Earthquake by Global DCNN and VAE-CNN Modeling -- Concluding Remarks.

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.

ISBN: 9789811936593$q(electronic bk.)

Standard No.: 10.1007/978-981-19-3659-3doiSubjects--Topical Terms:

196274
Earthquake prediction.

LC Class. No.: QE538.8 / .T67 2022

Dewey Class. No.: 551.22

Geochemical mechanics and deep neural network modelingapplications to earthquake prediction /
LDR:03037nmm a2200325 a 4500 001 628984
003 DE-He213
005 20220819210926.0
007 cr nn 008maaau
008 230224s2022 si s 0 eng d
020 $a 9789811936593$q(electronic bk.)
020 $a 9789811936586$q(paper)
024 7 $a 10.1007/978-981-19-3659-3 $2 doi
035 $a 978-981-19-3659-3
040 $a GP $c GP
041 0 $a eng
050 4 $a QE538.8 $b .T67 2022
072 7 $a RBGK $2 bicssc
072 7 $a SCI019000 $2 bisacsh
072 7 $a RBGK $2 thema
082 0 4 $a 551.22 $2 23
090 $a QE538.8 $b .T683 2022
100 1 $a Toriumi, Mitsuhiro. $3 886964
245 1 0 $a Geochemical mechanics and deep neural network modeling $h [electronic resource] : $b applications to earthquake prediction / $c by Mitsuhiro Toriumi.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2022.
300 $a xiii, 274 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advances in geological science, $x 2524-3837
505 0 $a Introduction -- Mechanics of Crack Sealing with Fluid Flow in the Plate Boundary -- Large Scale Permeable Convection of the Plate Boundary Zone -- Rapid Process of Massive Extrusion of Plate Boundary Rocks -- Mechanics by Synchronous GRACE Gravity, Earth Rotation, Plate Velocity and Global Correlated Seismicity -- Gaussian Network Model of Global Seismicity -- Prediction Testing of Plate Boundary Earthquake by Global DCNN and VAE-CNN Modeling -- Concluding Remarks.
520 $a 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.
650 0 $a Earthquake prediction. $3 196274
650 0 $a Geochemical modeling. $3 737386
650 0 $a Neural networks (Computer science) $x Mathematical models. $3 710533
650 1 4 $a Geochemistry. $3 196060
650 2 4 $a Geophysics. $3 197003
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Mathematics of Planet Earth. $3 779959
650 2 4 $a Natural Hazards. $3 348413
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Advances in geological science. $3 818796
856 4 0 $u https://doi.org/10.1007/978-981-19-3659-3
950 $a Earth and Environmental Science (SpringerNature-11646)