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Article . 2013 . Peer-reviewed
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PubMed Central
Article . 2013
License: CC BY NC ND
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Detecting Sedimentary Cycles using Autocorrelation of Grain size

Authors: Xiao, Shangbin; Li, Rui; Chen, Muhong;

Detecting Sedimentary Cycles using Autocorrelation of Grain size

Abstract

Detection of sedimentary cycles is difficult in fine-grained or homogenous sediments but is a prerequisite for the interpretation of depositional environments. Here we use a new autocorrelation analysis to detect cycles in a homogenous sediment core, E602, from the northern shelf of the South China Sea. Autocorrelation coefficients were calculated for different mean grain sizes at various depths. The results show that sediments derived from rapid depositional events have a better autocorrelation. Analysis of two other cores confirms this result. Cores composed of sediments deposited quickly under stable and/or gradually changing hydrodynamic conditions, have higher autocorrelation coefficients, whereas, those composed of sediments deposited during calm periods have relatively low autocorrelation coefficients. It shows that abrupt changes in autocorrelation coefficients usually indicate the existence of a boundary between adjacent sedimentary cycles, with each cycle beginning with a high positive autocorrelation coefficient of grain size and ending with a low negative one.

Related Organizations
Keywords

Geologic Sediments, Models, Statistical, Statistics as Topic, Computer Simulation, Particle Size, Article, Algorithms, Environmental Monitoring

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Average
Average
Green
gold