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Dynamic Modeling and Quantitative An...

Berry, Michael Aaron.

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Dynamic Modeling and Quantitative Analysis of Topography Development in Extensional Tectonic Settings.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dynamic Modeling and Quantitative Analysis of Topography Development in Extensional Tectonic Settings./
作者:	Berry, Michael Aaron.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	116 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
Contained By:	Dissertations Abstracts International81-10B.
標題:	Geophysics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27738268
ISBN:	9798607320942

Dynamic Modeling and Quantitative Analysis of Topography Development in Extensional Tectonic Settings.
Berry, Michael Aaron.

Dynamic Modeling and Quantitative Analysis of Topography Development in Extensional Tectonic Settings. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 116 p.

Source: Dissertations Abstracts International, Volume: 81-10, Section: B.

Thesis (Ph.D.)--New Mexico Institute of Mining and Technology, 2020.

This item must not be sold to any third party vendors.

This dissertation describes three studies that use numerical models and quantitative analyses to understand the roles of tectonics and surface processes on the evolution of topography in extensional settings. The first chapter explores mechanisms of gravitationally-driven collapse of orogenic plateaus using a finite element geodynamical code. We find that without lower-crustal channel flow pathways in adjacent lithosphere, syn-orogenic gravitational collapse of plateau lithosphere is difficult. Post-orogenic collapse in an extensional stress field is fast, and results in the development of rift basins on the collapsing plateau. The next chapter describes how tectonics, surface processes, and climate affect drainage basin development and the transition from open- to closed drainages in narrow continental rift basins. Our models predict that slow rift opening rates, wet cli- mates, and tectonic uplift can trigger closed-to-open drainage transitions. The last chapter of this dissertation illustrates how tens of thousands of synthetic landscapes, generated with a landscape evolution model, can be evaluated using machine learning and landscape metrics to understand landscape memory. We show that landscapes retain structural information from their past over millions of years of erosion.

ISBN: 9798607320942Subjects--Topical Terms:

535228
Geophysics.
Subjects--Index Terms:

Geomorphology

Dynamic Modeling and Quantitative Analysis of Topography Development in Extensional Tectonic Settings.
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300 $a 116 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
500 $a Advisor: van Wijk, Jolante.
502 $a Thesis (Ph.D.)--New Mexico Institute of Mining and Technology, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation describes three studies that use numerical models and quantitative analyses to understand the roles of tectonics and surface processes on the evolution of topography in extensional settings. The first chapter explores mechanisms of gravitationally-driven collapse of orogenic plateaus using a finite element geodynamical code. We find that without lower-crustal channel flow pathways in adjacent lithosphere, syn-orogenic gravitational collapse of plateau lithosphere is difficult. Post-orogenic collapse in an extensional stress field is fast, and results in the development of rift basins on the collapsing plateau. The next chapter describes how tectonics, surface processes, and climate affect drainage basin development and the transition from open- to closed drainages in narrow continental rift basins. Our models predict that slow rift opening rates, wet cli- mates, and tectonic uplift can trigger closed-to-open drainage transitions. The last chapter of this dissertation illustrates how tens of thousands of synthetic landscapes, generated with a landscape evolution model, can be evaluated using machine learning and landscape metrics to understand landscape memory. We show that landscapes retain structural information from their past over millions of years of erosion.
590 $a School code: 0295.
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650 4 $a Geomorphology. $3 542703
650 4 $a Applied mathematics. $3 2122814
653 $a Geomorphology
653 $a Landscape evolution
653 $a Machine learning
653 $a Modeling
653 $a Rifting
653 $a Tectonics
690 $a 0373
690 $a 0484
690 $a 0364
710 2 $a New Mexico Institute of Mining and Technology. $b Earth and Environmental Science. $3 3184320
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