Advances in Earth Science ›› 2020, Vol. 35 ›› Issue (4): 414-430. doi: 10.11867/j.issn.1001-8166.2020.030

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Review in Detrital Zircon U-Pb Geochronology: Data Acquisition, Analysis and Comparison

Ling Zhang 1( ),Ping Wang 2, 3( ),Xiyun Chen 2,Yong Yin 1   

  1. 1.School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023,China
    2.School of Geographical Sciences, Nanjing Normal University, Nanjing 210023,China
    3.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023,China
  • Received:2020-01-30 Revised:2020-03-03 Online:2020-04-10 Published:2020-05-08
  • Contact: Ping Wang
  • About author:Zhang Ling (1993-), male, Yancheng City, Jiangsu Province, Master student. Research areas include U-Pb dating provenance of detrital zircons. E-mail:
  • Supported by:
    the National Natural Science Foundation of China “Tracing the southward-flowing paleo-Jinsha River—Sedimentary records from the Paleogene basins along the southeastern margin of Tibetan Platea”(41572154)

Ling Zhang, Ping Wang, Xiyun Chen, Yong Yin. Review in Detrital Zircon U-Pb Geochronology: Data Acquisition, Analysis and Comparison[J]. Advances in Earth Science, 2020, 35(4): 414-430.

The U-Pb chronology of detritus zircon is an important method to explore sediment provenance, which is widely used in sedimentology, geotectonics, geomorphology and other fields. This paper reviewed the recent progress of the U-Pb chronology of detrital zircon from three aspects: data acquisition, analysis and comparison. In terms of data acquisition, the sample preparation method, isotope age data selection and test quantity were expounded from the basic principle; In terms of data analysis, the data visualization methods of Probability Density Plot (PDP), Kernel Density Estimate (KDE) and Cumulative Age Distribution (CAD) were compared; In terms of data comparison, the basic algorithm and application advantages of quantitative comparison were analyzed with examples, including (dis)similarity measures based on non-parametric hypothesis tests (K-S test), (dis)similarity measures based on age spectrum comparison (Cross-correlation coefficients) and (dis)similarity measures based on Multi-Dimensional Scali (MDS). Finally, three commonly used software tools were introduced. Suggestions were given in terms of data acquisition, analysis and comparison for future research.

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