東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Machine Learning and Plate Tectonic ...

Rahimzadeh Bajgiran, Moloud,

FindBook

Google Book

Amazon

博客來

Machine Learning and Plate Tectonic Analysis for Mantle Heterogeneity, Paleoclimate, and Critical Minerals /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning and Plate Tectonic Analysis for Mantle Heterogeneity, Paleoclimate, and Critical Minerals // Moloud Rahimzadeh Bajgiran.
作者:	Rahimzadeh Bajgiran, Moloud,
面頁冊數:	1 electronic resource (218 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
Contained By:	Dissertations Abstracts International86-06B.
標題:	Plate tectonics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31641171
ISBN:	9798346852292

Machine Learning and Plate Tectonic Analysis for Mantle Heterogeneity, Paleoclimate, and Critical Minerals /
Rahimzadeh Bajgiran, Moloud,

Machine Learning and Plate Tectonic Analysis for Mantle Heterogeneity, Paleoclimate, and Critical Minerals /Moloud Rahimzadeh Bajgiran. - 1 electronic resource (218 pages)

Source: Dissertations Abstracts International, Volume: 86-06, Section: B.

Nearly all the Earth's interior is inaccessible to direct observation, making it necessary to study the Earth's internal dynamics through forward or inverse models that rely on indirect measurements taken at or near the surface. In recent years, the amount and variety of data available for such models have expanded dramatically, leading to a corresponding rise in the use of numerical models for interpretation. Tomographic images of the Earth's interior, along with reconstructions of past plate motions, are critical components in many Earth science studies. However, the uncertainties inherent in these models are rarely acknowledged or examined in depth. In this work, we gain new insights into the Earth's interior and its dynamics by conducting an objective analysis of these models, with a focus on exploring and accounting for their uncertainties and biases. We analyze tomographic images to clarify their geodynamic interpretations, particularly in distinguishing between buoyancies caused by thermal versus compositional variations. We then employ the plate reconstruction model, Tomopac, which integrates updates from seismic tomography and mantle convection models, to improve our understanding of intra-oceanic subduction zones. We assess how updates in intra-oceanic subduction zones-derived from seismic tomography and mantle convection models-enhance our understanding of the deep carbon cycle and Earth's climate transitions, as well as the prediction of future porphyry systems. We integrate plate tectonic subduction models with machine learning techniques to create prospectivity maps for copper mineralization. This approach highlights the potential of our method in advancing global mineral exploration.

English

ISBN: 9798346852292Subjects--Topical Terms:

542702
Plate tectonics.
Subjects--Index Terms:

Copper porphyry system

Machine Learning and Plate Tectonic Analysis for Mantle Heterogeneity, Paleoclimate, and Critical Minerals /
LDR:03277nmm a22004573i 4500 001 2427582
005 20260911085206.5
006 m o d
007 cr|nu|||||||||
008 260929s2024 miu||||||m |||||||eng d
020 $a 9798346852292
035 $a (MiAaPQD)AAI31641171
035 $a AAI31641171
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Rahimzadeh Bajgiran, Moloud, $e author. $0 (orcid)0009-0009-4513-9237 $3 3814517
245 1 0 $a Machine Learning and Plate Tectonic Analysis for Mantle Heterogeneity, Paleoclimate, and Critical Minerals / $c Moloud Rahimzadeh Bajgiran.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (218 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
500 $a Advisors: Wu, Jonny Committee members: Beck, Susan; Persaud, Patricia; Colli, Lorenzo.
502 $b Ph.D. $c The University of Arizona $d 2024.
520 $a Nearly all the Earth's interior is inaccessible to direct observation, making it necessary to study the Earth's internal dynamics through forward or inverse models that rely on indirect measurements taken at or near the surface. In recent years, the amount and variety of data available for such models have expanded dramatically, leading to a corresponding rise in the use of numerical models for interpretation. Tomographic images of the Earth's interior, along with reconstructions of past plate motions, are critical components in many Earth science studies. However, the uncertainties inherent in these models are rarely acknowledged or examined in depth. In this work, we gain new insights into the Earth's interior and its dynamics by conducting an objective analysis of these models, with a focus on exploring and accounting for their uncertainties and biases. We analyze tomographic images to clarify their geodynamic interpretations, particularly in distinguishing between buoyancies caused by thermal versus compositional variations. We then employ the plate reconstruction model, Tomopac, which integrates updates from seismic tomography and mantle convection models, to improve our understanding of intra-oceanic subduction zones. We assess how updates in intra-oceanic subduction zones-derived from seismic tomography and mantle convection models-enhance our understanding of the deep carbon cycle and Earth's climate transitions, as well as the prediction of future porphyry systems. We integrate plate tectonic subduction models with machine learning techniques to create prospectivity maps for copper mineralization. This approach highlights the potential of our method in advancing global mineral exploration.
546 $a English
590 $a School code: 0009
650 4 $a Plate tectonics. $3 542702
650 4 $a Paleoclimate science. $3 3173835
650 4 $a Geophysics. $3 535228
653 $a Copper porphyry system
653 $a Critical minerals
653 $a Machine learning
653 $a Paleoclimate
653 $a Plate tectonic reconstructions
653 $a Seismic tomography
690 $a 0592
690 $a 0653
690 $a 0373
690 $a 0467
710 2 $a The University of Arizona. $b Geosciences. $e degree granting institution. $3 3814481
720 1 $a Wu, Jonny $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-06B.
790 $a 0009
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31641171