用Gazzi - Dickinson方法建立砂岩碎屑组成的全球数据集

IF 3.3 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Geoscience Data Journal Pub Date : 2023-09-01 DOI:10.1002/gdj3.212

Xiaolong Dong, Xiumian Hu, Wen Lai, Weiwei Xue, Shijie Zhang, Yiqiu Zhang, Wei An, Haiming Fan, Sijin Chen, Cui Li, Xingyun Wang, Yue Wu, Jinlv Chen, Yajun Zhang, Kun Yu

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引用次数: 0

摘要

砂岩碎屑组成是砂岩分类、物源分析、油气勘探等研究的重要数据。几十年来积累了大量的碎屑成分数据，但这些数据分散在出版物中，没有统一的标准。在这里，我们使用Gazzi - Dickinson方法从646份同行评审的出版物中构建了砂岩碎屑成分的全球数据集。该数据集共涉及前寒武纪至第四纪的19,861个样品。对于每个样品，我们都详细介绍了参考资料、地理信息、地质背景、沉积时代和原始数据。这是一个高质量的数据集，用于收集来自不同研究的每个砂岩样本的信息，并进行了标准化。该数据集可广泛应用于地层比较、物源分析、源汇过程的一般规律探索和地质工程等方面。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

A global dataset of sandstone detrital composition by Gazzi-Dickinson method

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A global dataset of sandstone detrital composition by Gazzi-Dickinson method

Detrital composition of sandstone is the most important data for siliciclastic studies including sandstone classification, provenance analysis, oil and gas exploration. A large amount of detrital composition data has accumulated over the past decades, however, they are scattered in publications without unified standards. Here we constructed a global dataset of detrital components of sandstones from 646 peer-reviewed publications using Gazzi-Dickinson method. A total of 19,861 samples from Precambrian to Quaternary are involved in this dataset. For each sample, we present details on reference information, geographic information, geological background, depositional age and the original data. It is a high-quality dataset for the information on each sandstone sample from different studies which was standardized. The dataset can be used widely, such as for stratigraphic comparison, provenance analysis, exploring the general laws of the source-to-sink process and geological engineering.

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来源期刊

Geoscience Data Journal GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES

CiteScore

5.90

自引率

9.40%

发文量

审稿时长

4 weeks

期刊介绍： Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered. An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices. Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.