2019 Vol. 46, No. S1
Article Contents

ZHONG Wanting, LI Yubin, LI Yuchang, LIU Chaoqiang, CHEN Xiaoshen. 2019. Dataset of Resource Reserves in Duobuza Porphyry Copper Deposit of Tibet[J]. Geology in China, 46(S1): 18-31. doi: 10.12029/gc2019Z103
Citation: ZHONG Wanting, LI Yubin, LI Yuchang, LIU Chaoqiang, CHEN Xiaoshen. 2019. Dataset of Resource Reserves in Duobuza Porphyry Copper Deposit of Tibet[J]. Geology in China, 46(S1): 18-31. doi: 10.12029/gc2019Z103

Dataset of Resource Reserves in Duobuza Porphyry Copper Deposit of Tibet

    Fund Project: China Geological Survey Project “ Comprehensive Integration and Services of Mineral Geology and Metallogenic Regularities in China (Mineral Geology Annals)” (DD20160346, DD20190379)
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  • Duobuza Copper Deposit in Tibet, the first large-scale porphyry copper deposit discovered along Pangong Lake-Nujiang River metallogenic belt in recent years, is situated in Duobuza tectonic magmatic arc in the northern margin of Pangong Lake-Nujiang River suture zone. It was formed during the evolution period of archipelagic arc-basin system of Pangong Lake-Nujiang River of Early Cretaceous with metallogenic age of 120 Ma. The deposit is related to quartz diorite porphyrite and granodiorite-porphyry intrusions in the strata of Meiriqiecuo Formation of Early Cretaceous. Boasting of present measured resources of Cu and Au of 2.30 million tonnes and 92 tonnes with average grade of 0.51% and 0.2 g/t respectively, Duobuza Copper Deposit ranks as one of the super large-scale deposits and one of the national strategic reserve bases of mineral resources. The dataset of resource reserves in Duobuza Porphyry Copper Deposit of Tibet is built based on the valuable data obtained in the mineral survey during 2005-2010. It contains 10 data tables, in which the grades and relevant calculated results of single-engineering industrial ore bodies and low-grade ore bodies in Duobuza Copper Deposit are mainly included. Furthermore, all detailed data related to reserve calculation are summarized and collated systematically in the dataset.

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