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Comparison of Supercontinent Cycles in the Metallogeny of Iron by Deposit Types and Explored Resources
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Comparison of Supercontinent Cycles in the Metallogeny of Iron by Deposit Types and Explored Resources
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Journal Article
Comparison of Supercontinent Cycles in the Metallogeny of Iron by Deposit Types and Explored Resources
2026
Overview
AbstractThe explored resources of 398 iron ore deposits (≥100 mln t Fe in each one) are distributed on the geological time scale according to their ages and types. This distribution is analyzed with an emphasis on comparison of supercontinent cycles. More than a half of all resources are related to the oldest Kenoran cycle. Almost all of them are concentrated in deposits of thin-banded iron quartzites of the Algoma and Superior types. In the following Columbian cycle, which is second in terms of overall productivity, the Superior type is again the leading deposit type. Orthomagmatic and postmagmatic types also account for a significant share of the resources of this cycle. The Rodinian cycle is the least productive with solitary large deposits, among which the most significant are representatives of the carbonatite and sedimentary-epigenetic types. The Pangean cycle is very slightly inferior to the Columbian cycle in overall productivity. It is the only one represented by all deposit types involved in the analysis. The largest resources are contained in the Rapitan-type iron quartzites, exceptional only to this cycle. Furthermore, within this cycle, the most significant resources for their types were formed by exhalative-sedimentary, carbonatite, and postmagmatic deposits. The Amasian cycle is half the size of the Pangean cycle in terms of explored resource base. It is the most productive cycle for sedimentary epicontinental deposits. Significant resources are also concentrated in postmagmatic deposits. It is concluded that each supercontinent cycle has a distinctly unique appearance in the iron metallogeny. Taking these specific features into account should improve the quality of predictive models for regional iron ore prospecting.
Publisher
Springer Nature B.V
Subject
