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Dissertation/Thesis Abstract

High-Resolution Methodology for Particle Size Analysis of Naturally Occurring Sand Size Sediment Through Laser Diffractometry WITH Application to Sediment Cores: Kismet, Fire Island, New York
by Dias, Kara Alexandra, M.S., State University of New York at Stony Brook, 2014, 95; 1563269
Abstract (Summary)

The detailed methodological measurements of very fine-grained sediments by laser diffraction have been reported to yield a very low analytical uncertainty. When coarser grained samples are run under the methodology recommended for fine-grained sediments, there is variability between the measurements, especially in the size fraction greater than 200 micrometers. This study seeks to refine the standard operating procedures of laser diffraction for grain size analysis of sand sized sediment as well as quantify the associated analytical uncertainty. The influence of selected methodological aspects on the results of the particle size distribution were assessed and optimal machine parameters, suspension mediums and sample preparation techniques were determined. It was found that for the investigated sands the following modifications to the standard operating procedures for fine grained sediment must be made: (1) bulk dry sieving of the samples must be introduced as a sample preparation step, (2) optimal obscuration occurred between 15-25 percent, (3) optimal pump speed was 2600 rpm. The associated analytical uncertainty is approximately ∼1.7 percent at 2 sigma. This enriched methodology allows for an efficient and accurate means of grain size analysis of naturally occurring sand sized sediment. .

The refined standard operating procedure is then applied to five sediment cores taken in a shoreline normal transect across Kismet, Fire Island, New York as well as modern sediments from the well-developed barrier island facies. The enhanced resolution associated with the refined methodology allows for grain size to begin to be used as a proxy of barrier island depositional environment and for sediment cores to be analyzed at centimeter scale intervals. The study confirms previous research that statistically analyzing grain size data can be used as a method for facies modeling. This study introduces a new method of recognizing clusters in the data through the use of an unsupervised k-means clustering algorithm. The algorithm can efficaciously be applied to the data as an unbiased, efficient way to recognize clusters in the statistically analyzed grain size data as well as in the grain size data plotted with depth. Successfully developing a high resolution method for grain size analysis of sand sized particles and using this method to analyze sediment core samples to test and confirm the methods of barrier island facies modeling of others, this study sets up the ability to take on further sedimentologic studies to address the evolution of barrier island systems through 3D subsurface modeling.

Indexing (document details)
Advisor: Sperazza, Michael
Commitee: Hanson, Gilbert N., Rasbury, Troy
School: State University of New York at Stony Brook
Department: Geosciences
School Location: United States -- New York
Source: MAI 53/05M(E), Masters Abstracts International
Subjects: Geology, Geomorphology, Sedimentary Geology
Publication Number: 1563269
ISBN: 978-1-321-11922-0
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