TY - DATA
T1 - 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
PY - 2021/11/15
AU - Xin Lin
AU - Domenico Cicchella
AU - Jun Hong
AU - Ganggang Meng
UR - https://data.4tu.nl/articles/dataset/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/17004049/1
DO - 10.4121/17004049.v1
KW - Whole-rock geochemistry
KW - zircon Hf isotopes
KW - Igneous Rocks
KW - Crustal growth
KW - deep learning
KW - Principal component analysis
N2 - The dataset contains the ages, thirty-five element compositions, and mean zircon ε<sub>Hf</sub>(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 SiO<sub>2</sub>, TiO<sub>2</sub>, Al<sub>2</sub>O<sub>3</sub>, MnO, MgO, CaO, Na<sub>2</sub>O, K<sub>2</sub>O, P<sub>2</sub>O<sub>5</sub>, 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<sub>Hf</sub>(t) values range from −30 to 30.<div>The dataset is anticipated to help generate and test hypotheses particularly about the evolution of Earth's crust.<div>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.<div>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.</div></div></div>
ER -