Six linear regression models for the magnetic susceptibility of sediment core GeoB18530-1

Six linear regression estimates of magnetic susceptibility were empirically tested for their ability to predict distinct HLs and ILs signal patterns. As predictors, we used MD magnetite content (IRM 20 mT), magnetic grain size (SIRM/X), dolomite and granite proxies (Ca/Sr and K/Fe), bulk porosity (%...

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Bibliographic Details
Main Authors: von Dobeneck, Tilo, Bukar, Shettima
Format: Dataset
Language:English
Published: PANGAEA
Subjects:
509-1
ArcTrain
Calculated from linear regression
DEPTH
sediment/rock
GC
GeoB18530-1
Geochemistry
grain size distribution
Grand Banks Margin
Gravity corer
Heinrich event
Heinrich events
Ice-Rafted Detritus
Linear regression equation
Linear regression model
Magnetic susceptibility
Maria S. Merian
MSM39
Processes and impacts of climate change in the North Atlantic Ocean and the Canadian Arctic
Residual value
rock magnetism
SE Grand Banks Slope
Climate change
North Atlantic
Online Access:https://doi.pangaea.de/10.1594/PANGAEA.971784
Description
Summary:Six linear regression estimates of magnetic susceptibility were empirically tested for their ability to predict distinct HLs and ILs signal patterns. As predictors, we used MD magnetite content (IRM 20 mT), magnetic grain size (SIRM/X), dolomite and granite proxies (Ca/Sr and K/Fe), bulk porosity (%), dolomite and granite counts, and counts of the major 13 IRD species. As the obtained numbers and residues demonstrate, the rock magnetic proxies yield the best fits, followed by element ratios and porosity. The count-based predictors are incapable of estimating the older HLs 3-6 and ILs 4-6 correctly; their regression coefficients also do not conform to the measured susceptibilities of the IRD species.

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