4.5 Article

An algorithm for U-Pb isotope dilution data reduction and uncertainty propagation

期刊

GEOCHEMISTRY GEOPHYSICS GEOSYSTEMS
卷 12, 期 -, 页码 -

出版社

AMER GEOPHYSICAL UNION
DOI: 10.1029/2010GC003478

关键词

U-Pb; geochronology; uncertainty propagation

资金

  1. National Science Foundation [EAR-0746205, EAR-0930166]
  2. Division Of Earth Sciences
  3. Directorate For Geosciences [0930223] Funding Source: National Science Foundation

向作者/读者索取更多资源

High-precision U-Pb geochronology by isotope dilution-thermal ionization mass spectrometry is integral to a variety of Earth science disciplines, but its ultimate resolving power is quantified by the uncertainties of calculated U-Pb dates. As analytical techniques have advanced, formerly small sources of uncertainty are increasingly important, and thus previous simplifications for data reduction and uncertainty propagation are no longer valid. Although notable previous efforts have treated propagation of correlated uncertainties for the U-Pb system, the equations, uncertainties, and correlations have been limited in number and subject to simplification during propagation through intermediary calculations. We derive and present a transparent U-Pb data reduction algorithm that transforms raw isotopic data and measured or assumed laboratory parameters into the isotopic ratios and dates geochronologists interpret without making assumptions about the relative size of sample components. To propagate uncertainties and their correlations, we describe, in detail, a linear algebraic algorithm that incorporates all input uncertainties and correlations without limiting or simplifying covariance terms to propagate them though intermediate calculations. Finally, a weighted mean algorithm is presented that utilizes matrix elements from the uncertainty propagation algorithm to propagate random and systematic uncertainties for data comparison between other U-Pb labs and other geochronometers. The linear uncertainty propagation algorithms are verified with Monte Carlo simulations of several typical analyses. We propose that our algorithms be considered by the community for implementation to improve the collaborative science envisioned by the EARTHTIME initiative.

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