Automatic identification of heavy mineral compositions in Zhejiang-Fujian rivers and implications for provenance analysis

CUI Yuhua; LIU Mengjia; HUANG Xiangtong; YANG Shouye; YUE Wei; ZHAO Xilin; JIN Guodong

doi:10.16562/j.cnki.0256-1492.2024091801

Marine Geology & Quaternary Geology > 2025 > 45(1): 79-95. > DOI: 10.16562/j.cnki.0256-1492.2024091801

CUI Yuhua，LIU Mengjia，HUANG Xiangtong，et al. Automatic identification of heavy mineral compositions in Zhejiang-Fujian rivers and implications for provenance analysis[J]. Marine Geology & Quaternary Geology，2025，45(1)：79-95. DOI: 10.16562/j.cnki.0256-1492.2024091801

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Automatic identification of heavy mineral compositions in Zhejiang-Fujian rivers and implications for provenance analysis

1.
State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China
2.
School of Geography, Geomatics & Planning, Jiangsu Normal University, Xuzhou 221116, China
3.
Nanjing Geological Survey Center, China Geological Survey, Nanjing 210016, China

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Received Date: September 17, 2024
Revised Date: October 07, 2024
Accepted Date: October 07, 2024
Available Online: November 20, 2024

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Abstract

Abstract

Heavy mineral composition in sediments is one of the primary analytical methods for provenance analysis. However, conventional manual identification of heavy minerals has problems such as large errors, small statistics, and poor repeatability, posing substantial challenges in revealing provenance characteristics and tracing sediment source and sink processes. Utilizing the TESCAN Integrated Mineral Analyzer (TIMA), we conducted heavy mineral analysis on 26 sediment samples from eight major coastal rivers in the Zhe-Min basin, including the Qiantang River, Jiaojiang River, Minjiang River, and so on. By comparing the results of manual identification and the geological background of the basin, the potential of TIMA heavy mineral analysis in provenance tracing was explored. A total of 209019 mineral grains were analyzed, and 36 different types of heavy minerals were distinguished. Compared with the manual identification methods, the identification results of the two methods for the main heavy mineral types and their relative content changes in the river are largely the same. However, TIMA takes less time, identifies more heavy minerals in quantity and types, and has good reproducibility of identification results. TIMA heavy mineral analysis results indicate that the main heavy mineral assemblages in the Zhe-Min basin included epidote group, amphibole group, and ferromagnetic metal minerals, of which zoisite is the predominant type and a typical heavy mineral of the basin. The amphibole group minerals are primarily composed of hornblende, followed by ferro-actinolite, with significant variations in content across different basin samples. The percentage of ferromagnetic metal minerals exceeds 40% in most samples, containing hematite, magnetite, limonite, titanium magnetite, and authigenic pyrite. Overall, the differences in exposed stratigraphic lithology in the northwestern Zhejiang, southeastern Zhejiang, northwestern Fujian, southwestern Fujian, and eastern Fujian areas reflect the distinct characteristics of the heavy mineral composition in Zhe-Min rivers. Principal component analysis showed that the higher positive loadings (＞0.88) of apatite, amphibole group, and xenotime are associated with the principal component (PC) 1, suggesting that magmatic source rocks influenced the PC 1. Conversely, the higher positive loadings of schorl, garnet group, and chlorite associated with PC 2 indicate influence from metamorphic source rocks. This study demonstrates that it is more efficient to systematic research on heavy minerals from rivers to the sea using automated mineral identification methods like TIMA in the study of heavy minerals with high accuracy requirements.
- Zhejiang-Fujian rivers flowing into the sea,
- heavy mineral composition,
- TIMA,
- principal component analysis

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References

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Automatic identification of heavy mineral compositions in Zhejiang-Fujian rivers and implications for provenance analysis