基于重磁资料的渭河盆地氦气资源分布规律

Chun-guan ZHANG,Bing-qiang YUAN,Yu-hong LI,Jing-guo CHEN,Qing-jia TONG

Progress in Geophysics（2017）

Cited 10|Views3

Abstract

为了深入研究渭河盆地氦气资源分布规律,本文基于1∶200,000航空磁力△T总场异常数据计算了航磁化极异常,并对布格重力异常及航磁化极异常数据利用正则化滤波方法进行了异常的分解,获取了渭河盆地局部重力异常及局部磁力异常.结合已有的地质及地球物理研究成果,对布格重力异常、局部重力异常、航磁化极异常以及局部磁力异常特征进行了深入分析,并就重力异常、磁力异常、主要断裂及岩浆岩与不同氦气含量的钻井分布之间的关系进行了深入研究,探讨了本区氦气资源的分布规律.研究结果表明,渭河盆地氦气资源主要分布于盆地南部宝鸡-周至-长安-渭南-潼关一带,其中咸阳、周至、长安、蓝田、华阴等地区氦气含量较高.渭河盆地氦气资源分布与本区断裂有密切关系,尤其是秦岭北缘断裂、渭河断裂以及临潼-长安断裂.渭河盆地南部氦气资源的分布还与该区发育的花岗岩有密切关系.

Translated text

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