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

doi: 10.4121/17004049.v1
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
Datacite citation style:
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
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Dataset
The dataset contains the ages, thirty-five element compositions, and mean zircon εHf(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 εHf(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.
history
  • 2021-11-15 first online, published, posted
publisher
4TU.ResearchData
format
.xlsx
organizations
Chang'an University, School of Earth Sciences and Resources, Xi'an, China