five

A new textural-genetic classification of phosphatic rocks

收藏
中国科学数据2026-03-03 更新2026-04-25 收录
下载链接:
https://www.sciengine.com/AA/doi/10.3724/j.1000-4734.2025.45.106
下载链接
链接失效反馈
官方服务:
资源简介:
To unify the classification and naming of rocks is conducive to the academic exchange and guidance of resource development. The national standard for the classification and nomenclature of phosphatic rocks adopts the textural-genetic classification as the primary criterion. Consequently, any advancements of researches on the genesis of phosphatic rocks will directly influence the classification and nomenclature scheme of phosphatic rocks. Based on the microscopic observations of typical phosphatic rocks in Guizhou, Yunnan, Sichuan, Hunan, and Hubei provinces, combined with comprehensive analysis of previous research achievements, this paper proposes a new textural-genetic classification scheme for phosphatic rocks by referencing the research methodology of carbonate microfacies. (1) The new textural-genetic classification pointed out that phosphatic rocks can be categorized into the allochthonous, autochthonous, recrystallized, mud-cracked, and silicified ones for reflecting their dynamic formation history. (2) A type of medium-grained clastic phosphatic rock is refined and supplemented, with clasts including intraclasts, bioclasts, coated grains, peloids, and lumps. Especially, the bioclast was regarded as granular component and was emphasized to have participated into the formation of phosphatic rock as its critical component. (3) The type of autochthonous phosphatic rock has been sub-classified to sub-types such as the stromatolitic, laminar, and clotted phosphatic rocks for reflecting different morphologies of algae, and a sub-type of continental sedimentary phosphite. 以These revised contents of the above proposed textural-genetic classification serve as foundational material for establishing a new edition of the national standard for the classification and nomenclature of phosphatic rocks.
创建时间:
2025-09-19
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作