T139 Zircon and Beyond: Quantitative Methods for Characterizing Provenance in Modern and Ancient Sediment Routing Systems

Monday, 23 September 2019
1:30 PM - 5:30 PM
Room 225AB, North Building

APATITE GRAINS SORTED INTO GROUPS USING RARE EARTH ELEMENT CONCENTRATIONS AND URANIUM-LEAD AGES

Paper number: T139-6
Program: Technical Sessions
Time: 2:50 PM – 3:10 PM

Authors

Abstract

In sedimentary rocks and modern sediments, mineral grains such as apatite and zircon from multiple provenance sources may be mixed together. A practical example of where this may be important is the interpretation of apatite fission track (AFT) data from a sandstone. To properly interpret AFT data from such a mixture of sources, it is necessary to bin apatite grains, and their associated AFT data, into discrete groups based on AFT-independent data such as chemical composition or isotopic age.

The Radial Plot uses the AFT ages and age errors to produce one or more vectors that point to an age or ages of presumed geological significance. Algorithm BinomFit deconvolves the AFT age probability density function into one or more age peaks, each peak yielding an age of presumed geological significance. Neither of these approaches requires any AFT-independent data. O’Sullivan et al. (2018; G3,19/9) use laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to measure rare earth element (REE) concentrations and uranium-lead (UPb) ages for apatite grains and sort them into groups using principle component analysis.

In this study, we sort apatite grains into groups using the following steps: 1) for each of approximately 10 different apatite standard species analyzed during a LA-ICP-MS session, measure REE concentrations and UPb ages and calculate the mean and standard deviation for each measured value for each standard, 2) derive a session-specific, best-fit line for each measured value that gives standard deviation as a function of mean among all standards, 3) for each unknown apatite grain (from an unknown group), use the derived best-fit lines to define an apatite grain group centered at the measured values for the unknown grain and bounded by 2 standard deviations about the measured values, and 4) search for and isolate other unknown apatite grains that exhibit measured values that fall within the bounds of the defined apatite grain group.

Geological Society of America Abstracts with Programs. Vol. 51, No. 5

doi: 10.1130/abs/2019AM-333777

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