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Astronomical and Stochastic Influenc...
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Aswasereelert, Wasinee.
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Astronomical and Stochastic Influences on Lacustrine and Marine Environments during the Cenozoic: Case Studies from the Green River Formation (Eocene) and the World's Ocean (Late Paleogene-Present).
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Astronomical and Stochastic Influences on Lacustrine and Marine Environments during the Cenozoic: Case Studies from the Green River Formation (Eocene) and the World's Ocean (Late Paleogene-Present)./
作者:
Aswasereelert, Wasinee.
面頁冊數:
155 p.
附註:
Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.
Contained By:
Dissertation Abstracts International76-02B(E).
標題:
Sedimentary geology. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3636321
ISBN:
9781321178944
Astronomical and Stochastic Influences on Lacustrine and Marine Environments during the Cenozoic: Case Studies from the Green River Formation (Eocene) and the World's Ocean (Late Paleogene-Present).
Aswasereelert, Wasinee.
Astronomical and Stochastic Influences on Lacustrine and Marine Environments during the Cenozoic: Case Studies from the Green River Formation (Eocene) and the World's Ocean (Late Paleogene-Present).
- 155 p.
Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.
Thesis (Ph.D.)--The University of Wisconsin - Madison, 2014.
This item must not be sold to any third party vendors.
Milankovitch theory provides a powerful deterministic framework for paleoclimate analysis, and astrochronology has become one of the most important methods for refining the geological time scale. Astronomical forcing/pacing is commonly perceived to exert a principal control on sedimentary deposition within marine and lacustrine environments during the Cenozoic, especially when lithologic cyclicity is prominent. In contrast, stochastic variability (noise) associated with the climate system and depositional environment is commonly under-explored or ignored in records for which Milankovitch cycles have been proposed. This dissertation applies novel quantitative cyclostratigraphic methodologies to demonstrate (1) how basinwide spatio-temporal data from the Eocene Wilkins Peak Member (Green River Formation) can be integrated and quantitatively analyzed to provide an objective basis for astronomical interpretation, and (2) how the evaluation of stochastic variance in marine benthic foraminifera delta 18O data - and its interplay with astronomical influence - can provide crucial information about paleoclimate evolution during the Cenozoic, especially with regards to the history of ice sheets. The Wilkins Peak analysis confirms a strong astronomical imprint on sedimentation, but also suggests a range of non-astronomical influences. Furthermore, the results lead to a new interpretation that the astronomical components were recorded by the alternation of carbonate-rich lacustrine and siliciclastic alluvial strata, instead of simple lacustrine deposition associated with lake level fluctuation. With respect to stochastic climate variability in benthic delta18O data spanning the past 34 Ma, climate noise reveals both new and previously proposed ice sheet growth events (including the Oi and Mi events), Antarctic glaciation, Northern Hemisphere glaciation, and mid-Pleistocene transition. This dissertation demonstrates the complexity and challenges inherent in cyclostratigraphy and paleoclimatology, and quantitative approaches to overcome some of these challenges. A complete understanding of the controls on both stratigraphy and paleoclimate cannot be reached if either deterministic or stochastic variance is totally ignored.
ISBN: 9781321178944Subjects--Topical Terms:
3173828
Sedimentary geology.
Astronomical and Stochastic Influences on Lacustrine and Marine Environments during the Cenozoic: Case Studies from the Green River Formation (Eocene) and the World's Ocean (Late Paleogene-Present).
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Milankovitch theory provides a powerful deterministic framework for paleoclimate analysis, and astrochronology has become one of the most important methods for refining the geological time scale. Astronomical forcing/pacing is commonly perceived to exert a principal control on sedimentary deposition within marine and lacustrine environments during the Cenozoic, especially when lithologic cyclicity is prominent. In contrast, stochastic variability (noise) associated with the climate system and depositional environment is commonly under-explored or ignored in records for which Milankovitch cycles have been proposed. This dissertation applies novel quantitative cyclostratigraphic methodologies to demonstrate (1) how basinwide spatio-temporal data from the Eocene Wilkins Peak Member (Green River Formation) can be integrated and quantitatively analyzed to provide an objective basis for astronomical interpretation, and (2) how the evaluation of stochastic variance in marine benthic foraminifera delta 18O data - and its interplay with astronomical influence - can provide crucial information about paleoclimate evolution during the Cenozoic, especially with regards to the history of ice sheets. The Wilkins Peak analysis confirms a strong astronomical imprint on sedimentation, but also suggests a range of non-astronomical influences. Furthermore, the results lead to a new interpretation that the astronomical components were recorded by the alternation of carbonate-rich lacustrine and siliciclastic alluvial strata, instead of simple lacustrine deposition associated with lake level fluctuation. With respect to stochastic climate variability in benthic delta18O data spanning the past 34 Ma, climate noise reveals both new and previously proposed ice sheet growth events (including the Oi and Mi events), Antarctic glaciation, Northern Hemisphere glaciation, and mid-Pleistocene transition. This dissertation demonstrates the complexity and challenges inherent in cyclostratigraphy and paleoclimatology, and quantitative approaches to overcome some of these challenges. A complete understanding of the controls on both stratigraphy and paleoclimate cannot be reached if either deterministic or stochastic variance is totally ignored.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3636321
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