超大陆
超大陆
地质学
弧(几何)
中国
古生物学
块(置换群论)
地球科学
克拉通
构造学
地理
考古
几何学
数学
作者
Fenglin Chen,Jian Wang,Xiaozhuang Cui,Shoufa Lin,Guangming Ren,Qi Deng,Mingda Huang,Kuizhou Li,Lijun Shen
标识
DOI:10.1016/j.precamres.2024.107432
摘要
Although the latest Mesoproterozoic tectonics of the southwestern Yangtze Block have been the focus of many recent studies, the paleogeographic configuration and related processes that controlled the South China Block with respect to the Rodinia assembly are still poorly understood. Here, an integrated dataset of zircon U–Pb geochronology, whole–rock geochemistry, and Sr–Nd–Hf isotopes of mafic intrusions from the Yonglang and Luji areas in the southwestern Yangtze Block is presented. Zircon U–Pb dating reveals that the mafic intrusions were emplaced at 1.03–1.01 Ga. These samples are divided into three groups based on mineralogical and geochemical characteristics. Group 1 meta-diabase samples exhibits calc-alkaline features and have obvious negative Nb-Ta-Ti anomalies and low zircon εHf(t) and whole-rock εNd(t) values. They may have been derived from an enriched lithospheric mantle source in an arc-related setting. Group 2 amphibolites are characterized by higher Nb contents (8.3–12.8 ppm), Nb/U, (Nb/Th)N, and (Nb/La)N ratios, resembling those of Nb-enriched basalt derived from a metasomatized mantle wedge. Group 3 meta-diabase samples have higher TiO2 (1.7–2.8 wt%), MgO (5.4–6.5 wt%), and Fe2O3t (13.1–17.4 wt%), with obvious negative Nb-Ta anomalies and high positive zircon εHf(t) values, indicative of derivation from a depleted mantle affected by subducted components. Integrating previous studies with our new results, an arc–back-arc system, with northward subduction (current coordinates), is suggested to have been developed in the southwestern Yangtze Block during the latest Mesoproterozoic. Furthermore, we suggest that the amalgamation of the current united Yangtze Block may occur at ca. 0.9 Ga, which is more likely linked to the assembly of the Rodinia supercontinent.
科研通智能强力驱动
Strongly Powered by AbleSci AI