Southeastern Section - 67th Annual Meeting - 2018

Paper No. 25-9
Presentation Time: 11:00 AM


PULLEN, Alex, Department of Environmental Engineering and Earth Sciences, Clemson University, 342 Computer Court, Anderson, SC 29625, IBANEZ-MEJIA, Mauricio, Department of Earth and Environmental Sciences, University of Rochester, Rochester, NY 14627, GEHRELS, George E., Department of Geosciences, University of Arizona, Gould-Simpson Building #77, 1040 E 4th St, Tucson, AZ 85721 and IBANEZ-MEJIA, Juan C., I. Physikalisches Institut, Theoretical Astrophysics Group, University of Cologne, Zülpicher str. 77, Köln, 50937, Germany

Advancements in instrumentation over the past two decades have allowed for the rapid measurement of U, Th, and Pb isotopes for geochronologic applications. Some of the greatest advancements have come in the area of laser-ablation inductively-coupled-plasma mass spectrometry (LA-ICP-MS). As a result of this progress, individual sample U-Pb datasets have grown by one to two orders of magnitude, and can now reach n≈ 1000 per sample without requiring extraordinary effort. This dividend is most notable in the application of dating detrital minerals and its application to understanding sedimentary systems and tectonic processes. Most LA-ICP-MS laboratories have the capacity to U-Pb date n= 1000 minerals in <7 hr., and determine ages with sufficient precision and accuracy—typically better than ±2% for both—to make sound and geologically meaningful interpretations. The laws of statistics dictate that Large-n (e.g., n≥ 300) datasets significantly increase the probability that exotic detrital components are identified within a sample aliquot relative to datasets with much fewer observations (e.g., n≈ 100). Additionally, as n increases, the measured distribution of ages should more closely resemble the ‘true’ distribution and relative abundance of the major age modes from a sample, therefore opening the door to more robust statistical treatment. These principles combined, in addition to future developments in statistical treatment of Large-n data, will allow for more robust inter-sample comparisons.