In:
Acta Geologica Sinica - English Edition, Wiley, Vol. 96, No. 6 ( 2022-12), p. 2148-2157
Abstract:
Garnet 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.
Type of Medium:
Online Resource
ISSN:
1000-9515
,
1755-6724
DOI:
10.1111/1755-6724.14921
Language:
English
Publisher:
Wiley
Publication Date:
2022
detail.hit.zdb_id:
2420386-5
SSG:
6,25
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