The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/17004049

Lin, Xin; Cicchella, Domenico; Hong, Jun; Meng, Ganggang (2021): Dataset of Element Compositions and Mean Zircon Hafnium Isotopes of Igneous Rocks underlying the research: A test of the hypothesis that syn-collisional felsic magmatism contributes to continental crustal growth. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/17004049.v1

Dataset

• Geochemistry

Crustal growth, deep learning, Igneous Rocks, Principal component analysis, Whole-rock geochemistry, zircon Hf isotopes

CC0

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by Xin Lin , Domenico Cicchella , Jun Hong , Ganggang Meng

The dataset contains the ages, thirty-five element compositions, and mean zircon ε_Hf(t) compositions of igneous rocks. The data is extracted from the database GEOROC and Tibetan Magmatism Database. The lithology in the dataset includes andesitic, anorthositic, basaltic, dacitic, dioritic, gabbroic, granitic, monzonitic, rhyolitic, and ultramafic rocks. The proportion of acidic, intermediate, mafic, and ultramafic rocks are 55%, 35%, 9%, and 1%, respectively. The data are temporally concentrated in Jurassic and early Cretaceous (n = 384), then in Precambrian (n = 344), late Cretaceous (n = 330), Trassic (n = 235), Cenozoic (n = 230), Permian (n = 206), Carboniferous (n = 152), and Silurian and Ordovician (n = 133). The elements include SiO₂, TiO₂, Al₂O₃, MnO, MgO, CaO, Na₂O, K₂O, P₂O₅, V, Ni, Rb, Sr, Y, Zr, Nb, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, Ta, Th, and U. The sample ages span from Proterozoic to Cenozoic, and the mean zircon ε_Hf(t) values range from −30 to 30.The dataset is anticipated to help generate and test hypotheses particularly about the evolution of Earth's crust.It is the dataset underlying the research/article: A test of the hypothesis that syn-collisional felsic magmatism contributes to continental crustal growth via deep learning modeling and principal component analysis of big geochemical datasets.In addition to direct utilization of raw data, advanced data science such as supervised/unsupervised machine learning algorithms can be applied to extract implicit geologic information.

• 2021-11-15 first online, published, posted

4TU.ResearchData

.xlsx

Chang'an University, School of Earth Sciences and Resources, Xi'an, China