鄂尔多斯盆地延长组长7段沉积岩元素地球化学特征及沉积环境分析

Huanhuan MA,Chiyang LIU,Long ZHANG,Dongdong ZHANG,Wenqing WANG, Yan ZHAO, Ming GAO,Xiaoyuan QUAN

Geoscience（2019）

Cited 8|Views14

Abstract

沉积岩的地球化学特征可以反映盆地沉积环境.为了探讨鄂尔多斯盆地延长组沉积时期古气候、氧化还原条件、古盐度等特征,对鄂尔多斯盆地东南部铜川延长组长7段的5个砂岩、8个页岩样品进行主量、微量以及稀土元素地球化学分析.结果显示,长7段细粒沉积物Al2O3/SiO2值与K2O/Na2O值指示样品成分成熟度差别不大,黏土矿物含量较高.ΣREE平均值为132.65×10-6,明显低于北美页岩平均稀土元素含量,也低于全球平均大陆上地壳值;LREE分异程度较高,呈微弱的Eu正异常,无Ce异常.“C值”、Sr/Cu、Rb/Sr、Mg/Ca等指标显示延长组沉积期整体处于温暖潮湿的气候区.研究区样品富集微量元素U、Sc、Cs(平均20.11 ×10-6、9.66×10-6、6.08×10-6),结合V、δU、Th/U、V/Cr、V/Sc、V/(V+ Ni)等指标分析,结果指示延长组沉积期以底层水体呈强分层的还原环境为主.Li、Sr、Ni、Sr/Ba、(MgO/Al2O3)×100等值显示沉积期水体总体为淡水-微咸水.

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper