Hunting the Datable Garnet using the LA‐ICP‐MS U‐Pb Method: Predicting Garnet U Concentration, based on Major and Minor Elements

Abstract

AbstractGarnet occurs in a wide range of rock types, from mantle peridotites to granites, from eclogites to skarns. In recent years, garnet LA‐ICP‐MS (Laser Ablation Inductively Coupled Plasma Mass Spectrometry) U‐Pb dating has provided a powerful solution for retrieving the ages of rock formations, but successful dating is often prohibited by the low concentration of U. However, the concentration of U, a trace element of garnet, is unknown prior to the LA‐ICP‐MS analysis. In this study, we propose that the U concentration in garnet can be predicted by the contents of major and minor elements, which can be quantitatively obtained by EPMA (electron probe microanalysis). Using a supervised machine learning method (neural network), a model is trained to discriminate U‐rich (>2 ppm) and U‐poor (<2 ppm) garnets, based on EPMA results. Results of cross validation shows that the model has an average accuracy of ∼92% and is a powerful tool in detecting datable U‐rich garnet. To facilitate the use of the discriminator, it is programmed as a stand‐alone Microsoft Excel spreadsheet (HighUGarnet) and users directly paste the molar proportions of garnet end members into it and obtain the discrimination result.